A key factor for fall prevention involves understanding the pathophysiology of stability. This study proposes the postural stability index (PSI), which is a novel measure to quantify different stability states on healthy subjects. The results of the x-, y-, and z-axes of the acceleration signals were analyzed from 10 healthy young adults and 10 healthy older adults under three conditions as follows: Normal walking, walking with obstacles, and fall-like motions. The ensemble empirical mode decomposition (EEMD) was used to reconstruct the acceleration signal data. Wearable accelerometers were located on the ankles and knees of the subjects. The PSI indicated a decreasing trend of its values from normal walking to the fall-like motions. Free-walking data were used to determine the stability based on the PSI. The segmented free-walking data indicated changes in the stability states that suggested that the PSI is potentially helpful in quantifying gait stability. Each year, approximately one third of older adults aged >65 years experience falls . Falls cPrevious studies focused on the fall detection method ,4,5. HowA previous study used dynamic stability to determine postural stability . The accIn 2014, Cui et al.  used ensThe current study proposed a measure that can distinguish different stability states in healthy subjects using an accelerometer. This study used EEMD and multiscale entropy (MSE) to develop the measure. The performance of EEMD was also evaluated by comparing its performance with the wavelet transform method. The organization of this paper is prepared as follows. The methods section provides the experiment protocol and a brief description of previous postural stability measures as well as the theoretical background behind EEMD, MSE, and wavelet transform methods. Results are then presented in Ten young adults (24 ± 0.94 years) and 10 older adults (69 ± 6.77 years) were recruited. All subjects were free of any postural stability-related disorder based on self-reports. The study was approved by the Institutional Review Board and informed consent forms were obtained from all subjects before their participation. The subjects were asked to perform walking, walking with obstacles, free walking, and fall-like motions . Young aTwenty healthy subjects with no history of falling in the past two years were grouped by age. Ensemble empirical mode decomposition (EEMD) was introduced to overcome the mode mixing phenomenon in empirical mode decomposition (EMD). Given signal intermittence, mode mixing situations typically occur during the EMD decomposition process. Mode mixing occurs when a single intrinsic mode function (IMF) either consists of oscillations with different frequencies or a signal with a similar frequency is in different IMF components. Mode mixing can change the physical meaning of each IMF component by replacing the part of the IMF and driving it to the next IMF, thereby falsely suggesting that different physical processes may exist in an IMF . White noises of the same length were added to the original signal to form a mixture signal, xi(t), and subsequently the EMD decomposition was performed to obtain n layers of IMFs . We assumed that the process was conducted for m times, and this is defined as the number of ensemble members. The original signals were subsequently added by m white noises of the same energies to form m mixture signals. In the current study, white noises of an amplitude that was about 0.2 standard deviation of the signals were added [The purpose of adding the white noise of the finite amplitude to the signal was to populate the whole time-frequency space uniformly with the constituting components of different scales. The white noise cancels each other out in the time-space ensemble mean. Thus, only the true and physically meaningful signal can survive in the EEMD .0, x1, …xm, into one high-pass wavelet coefficient series and one low-pass coefficient series.Wavelet transform decomposes a signal into a set of basic functions by scaling and shifting the mother wavelet function. These basic functions are called wavelets because they wave up and down across the axis, and integrate to zero. Wavelets are highly effective in analyzing non-stationary signals, such as for noise reduction . The disWavelet packet decomposition (WPD) is a continuous wavelet transform. The difference between DWT and WPD is how the scale parameter is discretized. The WPD discretizes the scale more finely than DWT. Thus, it gives a better frequency resolution for the decomposed signal . HoweverFollowing its introduction by Costa et al. , multiscMSE proposed a method to measure complexity by constructing a consecutive coarse-grained time series by averaging a successively increasing number of data points in non-overlapping windows as follows:ength, m . The complexity index (CI) is obtained from the total value of the SampEn as a function of the scale factor as follows: The step stability index (SSI) was proposed to distinguish the walking patterns of fallers from non-fallers . The magThe non-fallers exhibited a higher SSI value that indicated a more stable gait pattern because the energy (as measured by the standard deviation) of the component at the step frequency exceeds the energy of higher frequency components that are potentially related to subtle unsteadiness of stepping. The SSI is used to quantify gait dynamics and discriminates between subjects with and without a history of falls. However, it is potentially not sufficiently sensitive to distinguish the postural stability of healthy subjects. The difference of the postural stability in non-fallers is potentially not as evident. Thus, the SSI may not be adequate to measure the likelihood of falls or to provide a better understanding as to humans’ maintenance of stability given that it distinguishes non-fallers from fallers without the ability to understand how postural stability corresponds to the movement of non-fallers.Dynamic stability parameters include the ratio of root mean square (RMS), step and stride regularity, and sample entropy (SampEn) . The ratMachine learning can be used to evaluate the accuracy of measurement methods. This current study used Fisher’s linear discriminant analysis (LDA), artificial neural network (ANN), support vector machines (SVM), and random forest (RF). The LDA is the oldest classifier, and it is used to find a linear combination, which characterizes two or more classes of objects or events. The ANN has been used extensively in classification problems, and it can be understood as a parallel-distributed processing system. This study used multilayer perception (MLP) as it is the most used and powerful neural network . The SVMDynamic stability can distinguish the different stability patterns due to ageing and illness. However, the approach cannot detect the difference between normal walking and walking with obstacles in healthy subjects, as shown in As shown in Each resultant of the x-, y-, and z-axes of the acceleration signal was decomposed using the 8-modes EEMD. This study used the resultant as opposed to a single axis to avoid information loss. Body sway indicates poor postural stability-ability and can occur in any axis of the signal. The complexity index of each IMF was calculated and subsequently normalized by dividing the complexity index of each IMF by the total complexity index of all IMFs to minimize the influence of individual differences. CI denotes the complexity index obtained from the MSE.The postural stability index (PSI) is defined as follows:The IMF3 was selected as the dominant IMF considering its frequency is closely related to the frequency of walking. Based on past studies, the range of walking frequency was 1.4–2.5 Hz while less stable movement exhibited a lower frequency ,28. TablThe results of the Pearson correlation showed that the IMF3 was correlated with the gait variability parameters of dynamic stability, as shown in p = 0.005). Conversely, there was no significant difference for the SSI (p > 0.05).The differences of the SSI and PSI values for both young and older adults are shown in p > 0.05). Conversely, the dynamic stability did not distinguish between normal walking and walking with obstacles  performed the worst in both age groups. The poor performance of the SSI happened because the SSI only considers the vertical axis data. Conversely, the PSI and dynamic stability consider the data in all axes since instability can occur in any axis. However, other than the ankle data of young adults, the accuracies for dynamic stability were generally low. The poor performance of the SSI and dynamic stability for older adults potentially occurred because those methods could not distinguish between the different activities of healthy subjects. Thus, the activities were not classified correctly.Interestingly, the performance of the PSI dropped when using DWT and WPD as the decomposition method. Conversely, the performance of the SSI increased although it was not as good as the performance of the PSI with EEMD. Other than that, the performance of the ankle improved while the performance of the knee decreased. The poor performance of wavelet decomposition compared to EEMD might be because of the short data records (60 s). Other than that, EEMD was able to estimate the subtle changes that were obtained via the first temporal derivative of the phase angle time series, scaled by the sampling rate. The decomposition by wavelet was not optimal because of frequency smoothing and it assumed frequency stationarity during the time span of the wavelet. The results in the current study are different from a previous study evaluating the performance of EMD and wavelets , becauseIn contrast to the previous study , the decTo better quantify the different stability states, the PSI values of the normal walking, walking with obstacles, and fall-like motions were evaluated. The PSI of the normal walking was used to determine the postural stability limit of each subject with the assumption that normal walking was at least 80% of the upper limit. Subsequently, the scales were developed by normalizing each PSI of the less stable movement to the upper limit value. There were small differences between young and older adults. This indicated that the MSE can eliminate the range variations between young and older adults. The normalization was calculated individually, and thus the similarity in the ranges was not equal to the same index values for all subject groups. The values of the MSE in young subjects were generally higher than those in older adults although the percentage of each movement when compared to the upper limit of postural stability for both young and older groups, were significantly similar. Therefore, the stability scales for both young and older adults can be unified as follows.80–100%: Stable.70–79%: Fairly stable, requires minor attention.45–69%: Unstable, requires high attention.<45%: Danger, may cause fall.To determine the walking stability of the subjects, the free-walking data were evaluated using the stability index. The evaluation was divided into two parts, namely general stability determination and segmented stability state determination. In the general stability determination, the stability was determined from the data across the entire 60 s period. Conversely, in the segmented determination, stability was evaluated every 10 s.Twenty subjects  were asked to perform a free-walking task to determine the stability of their walk in normal circumstances. The subjects were instructed to move as they liked without any intervention. As shown in With respect to the subject, E7, complaints related to the free-walking task were absent. However, this subject walked slower when compared to that in the normal walking and walking with obstacles tasks. This subject also paused several times during his free-walking task. This potentially occurred because the instruction involved walking freely as per the subjects’ wishes, and thus the subject, E7, was potentially not sufficiently motivated to perform his best in the task.Other unstable movements were detected on the knee for subjects, Y3, Y5, E1, and E3. With respect to the situations during the experiment, the results did not indicate those subjects performed poorly in the free-walking task. With respect to Although the stability of a movement can be considered as stable in general, it is possible that the postural stability quality of the movement is not the same all the time. Less stable movement can occur in a particular time interval. To evaluate the quality of the movement, the free-walking data of the subjects, Y3, Y5, E1, E3, and E7, were further analyzed. The sensor location on the knee was more accurate, and thus only the knee data were analyzed to determine the stability states. Thus, 60 s of walking data was divided into six segments, and postural stability was evaluated every 10 s, as shown in As shown in The PSI was able to evaluate the postural stability states of movement. However, given the individual differences in human movement, it was only relevant to individually evaluate the movement. The approach represented the characteristics of the stability of a particular subject. However, it was not possible to use the PSI to immediately analyze the movement. Stable state data are required to categorize the movement stability of the subject in question.Quantification of human stability is required to understand the mechanism underlying balance control. The contribution of this study involves providing a novel measure of the postural stability index (PSI) to distinguish between different postural stability states in healthy individuals. The PSI discriminated between normal walking and walking with obstacles in healthy subjects while SSI and dynamic stability did not. A previous study indicated that the SSI differentiates non-fallers from fallers. However, the SSI did not capture the differences between two walking tasks in healthy subjects. This potentially occurred because the differences were excessively small, and the SSI algorithm only used vertical acceleration data in the evaluation. Conversely, the PSI used the resultant of the three-axes acceleration data by assuming that postural sway can occur in any axis.The present study involved several limitations as follows: (1) The present study used normal walking data to determine the upper postural stability limit. Therefore, the PSI cannot be used to quantify stability without existing normal walking data; (2) the present study used six IMFs to develop the PSI, and thus future studies are required to examine the applicability of the PSI for other decompositions with a different number of IMFs; (3) the present study used wearable accelerometers, and thus future studies are necessary to investigate the effect of the sensitivity of the accelerometers on the PSI; and (4) with respect to older adults who did not perform free-falls due to safety concerns, a past study compared the intentional and unintentional falls and indicated that the difference between the fall-like motions and falling only corresponds to the inclination angles . TherefoIn conclusion, the results of the present study suggest that the EEMD and MSE algorithm can be utilized to quantify postural stability in healthy subjects. The PSI method adequately measures different postural stability states in healthy subjects.


Microplot extraction (PE) is a necessary image processing step in unmanned aerial vehicle- (UAV-) based research on breeding fields. At present, it is manually using ArcGIS, QGIS, or other GIS-based software, but achieving the desired accuracy is time-consuming. We therefore developed an intuitive, easy-to-use semiautomatic program for MPE called Easy MPE to enable researchers and others to access reliable plot data UAV images of whole fields under variable field conditions. The program uses four major steps: (1) binary segmentation, (2) microplot extraction, (3) production of ∗.shp files to enable further file manipulation, and (4) projection of individual microplots generated from the orthomosaic back onto the raw aerial UAV images to preserve the image quality. Crop rows were successfully identified in all trial fields. The performance of the proposed method was evaluated by calculating the intersection-over-union (IOU) ratio between microplots determined manually and by Easy MPE: the average IOU (±SD) of all trials was 91% (±3). One of the major aims of investigations to improve agricultural machinery has been to reduce yield gaps . The wayIn recent years, great advances in sensors, aeronautics, and high-performance computing, in particular, have made high-throughput phenotyping more effective and accessible . The chaUnmanned aerial vehicles (UAVs) have become one of the most popular information retrieval tools for high-throughput phenotyping, owing to the reduced costs of purchasing and deploying them, their easier control and operation, and their higher sensor compatibility. Proposed image analysis pipelines for extracting useful phenotypic information normally include several steps: 3D mapping by structure-from-motion (SfM) and multiview stereo (MVS) techniques, generation of orthomosaic images  and digital surface models, extraction of microplots, and extraction of phenotypic traits. Among these steps, microplot extraction  is essential because it allows fields  to be examined at a level of detail corresponding to the level of accuracy of technologies such as the yield monitoring and seeder technologies mentioned above, ultimately providing better results , 8.Manually, usually by using geographic information system (GIS) softwareSemiautomatically, which still requires some manual inputAutomatically, which requires no manual inputThere are three main ways to perform MPE:To the best of our knowledge, no fully automatic microplot extraction method has been published so far.Several semiautomatic techniques have been developed to extract microplots from UAV images. A method proposed by Hearst  takes adAll of these techniques generally start with an orthomosaic image of the field rather than with raw images . The consequent loss of image quality is an important consideration for high-throughput phenotyping.Most MPE is still done manually, such as using shapefiles , the ArcIn this paper, we propose a semiautomated MPE solution based on image analysis. We tested two kinds of crops on the proposed method on six different field datasets. Sugarbeet and soybeen were chosen as test fields because both crops are of importance for feeding humanity , 17. TheThe preprocessing step is followed by MPE. To extract microplots, planted areas of the field are identified by summing the pixels classified as vegetation and then calculating the average. Microplot columns (90-degree rotation from crop rows) are identified by comparing vegetation pixel values with the average and erasing those below a threshold value. The same procedure is then applied to the column images to identify crop rows within each microplot column. The initial orthomosaic is then cropped to the borders identified by the program, and a shapefile is generated for each microplot to facilitate further image manipulation in GIS software or custom programs. Microplot intersection points are also saved in an Excel spreadsheet file.Finally, if raw unprocessed aerial images and SfM files  are available, the program can directly extract the identified microplots from the raw images so that the microplots will contain the maximum level of detail.Six field datasets were used as experimental units . The plaBefore image acquisition, the waypoint routing of the UAV was specified by using a self-developed software tool that automatically creates data formatted for import into Litchi for DJI drones . This readable waypoint routing data allowed highly accurate repetition of flight missions so that all images had the same spatial resolution. Orthomosaics of the georeferenced raw images were then produced in Pix4Dmapper Pro software  with thThe main aim of the program is to identify columns, and crop rows within each column, on an orthomosaic image of a field so that microplot areas can be extracted. Additional outputs that the user may need for further analysis of the data are also provided. The program comprises four processing steps: binary segmentation (preprocessing), microplot identification and extraction, production of shapefile, and additional production of microplots extracted from raw individual images by reverse calculation.The code was run on a laptop computer  connected to an external graphics processing unit .R, G, and B are the normalized red, green, and blue channel values of a pixel.The ExG vegetation index (step C) and the Otsu threshold (step D) are applied to each pixel of an orthomosaic image to produce a segmented binary image . Excess In the case of a very bright or very dark environment, where the Otsu threshold does not perform well –36, EasyMPE is the core step of the program. In this step, binary images of diverse field layouts are segmented (partitioned) into columns and crop rows.First, the image is rotated (step E) using the coordinates of the bounding box drawn around the binary image. In step A, it is important to draw the boundaries of the region of interest so that no object that might be segmented lies between the boundaries and the crop; otherwise, the image will not be correctly rotated.x-axis of the image, based on the sum of the white pixels distributed in the columns. To make the columns even, the binary image is slightly manipulated with an erosion followed by a horizontal dilatation. White pixels along the x-axis are first summed, and their average is calculated. Then, all values that are less than 1/3 of the average are erased. The erased pixels represent “transition parts” at either end of the crop rows as well as small intercolumn weeds. Columns are then identified (step F) by scanning along the x-axis: a transition from black to white pixels marks the left edge of a column, and the subsequent transition from white to black pixels marks its right edge.Second, columns are extracted by identifying local maxima along the x-axis than the columns identified in step F, the intrarow variation in the sum of the white pixels is expected to be small; moreover, interrow weeds are expected to be frequent. Thus, values less than 1/2 of the average sum are erased in step H. Then, crop rows are identified by scanning along the y-axis and identifying transitions in the segmented image from black to white or from white to black pixels.Third, on the orthomosaic binary image, crop rows are identified within each column (step H). Because crop rows are more homogeneous along the According to the number of crop rows and the number of columns in each field input into the program, microplots are cropped and saved (step I). Their coordinates are calculated in the orthomosaic coordinate system, or if the orthomosaic does not include coordinates, from the positions of the pixels on the image.The program can output shapefiles of the microplots.Shapefiles are produced by using the coordinates calculated during the MPE step, with the same characteristics as the orthomosaic image.Reverse calculation is the process of projecting individual microplots determined from the orthomosaic image back onto the corresponding area of the raw images. It is aimed at preserving the raw image resolution, instead of the lower quality of the orthomosaic image , for estX, Y, Z) are the 3D coordinates of the microplot: the values of X and Y are obtained during the microplot extraction step, and Z is the average height of the microplot in the DSM;  are the corrected 3D coordinates of the microplots after they have been fit to the P-Matrix (PMat) coordinates;  are intermediate 3D coordinates in the camera's coordinate system that are used to convert 3D points into 2D points; and  are the 2D pixel coordinates of the microplot in the raw image.For the field trials, the following Pix4Dmapper Pro  outputs u, v) corner coordinates of the microplot in the raw image). This output was chosen to exclude unwanted data that would unnecessarily increase computation times while including the information necessary for the user to be able to easily use the data.Easy MPE outputs a ∗.csv file with 11 columns , noise, number of columns, number of crop rows per column, and global orientation of the columns in the orthomosaic . The noise parameter is used after the segmentation. Objects smaller than the input value are removed in order to output a homogeneous image without signals labeled as noise by the user.For each dataset, the inputs were either self-evident  or determined from the field conditions and image resolution . The targeted area in the orthomosaic  had to be manually delimited so that the program would be applied to the desired area.Datasets 5 and 6 were binary images, and datasets 1 to 4 were RGB images. One image, dataset 4, had to be resized because it was too large for our computer to handle.Because the targeted area was manually delimited by the user, the program outputs could change slightly between trials.Input details are available in Table To evaluate the performance of Easy MPE, we produced reference microplots manually and used them as ground-truth data. The desired program output consists of microplots, each having the number of columns and crop rows specified by the user, so that the user's experimental design can be fit to the field or so that local field information can be determined more precisely.Reference microplots were delimited by hand on orthomosaic images, as precisely as possible until the level of accuracy was judged by the user to be sufficient. We aimed to minimize the exclusion of the target-MPE plant pixels and minimize the inclusion of adjacent rows or columns. Shapefiles for the reference images were produced by using the grid tool in QGIS Las Palmas v. 2.18.24 software . The freAlthough the manually produced reference microplots may include bias introduced by the user, their use does not compromise the study's aim, which was to compare program-delimited microplots with manually delimited ones.The manually determined microplots were automatically compared with the program-generated microplots by using the saved coordinates in their shapefiles and a Python program. This program uses pyshp v. 2.0.1  to retriWe used the intersection-over-union (IOU) performance criterion  to evaluIOU is calculated as:The output is a ∗.csv file with six columns: plot identification number, the program ∗.shp area, the manually determined ∗.shp area, the intersection area, the union area, and the IOU.n is the number of microplots in the targeted field, xi is the IOU ratio of microplot i, and The population standard deviation is calculated for the IOU asP and recall R are defined by equations (Precision quations  and 9),,P and reTP indicates a true positive: what has been correctly considered by the program to be part of a microplot; thus, TP = manualarea∩programarea  area is wholly included in the manually determined (program-determined) area, then P(R) will be equal to 100%.Thus, P and R.We also calculated the standard deviations of The program successfully identified microplots in all trial fields .The mean IOU among the datasets was 91% (±3%), indicating a 91% overlap between program-determined and manually determined microplot areas . MoreoveMean precision and recall were 95% (±2%) and 95% (±2%), respectively. These results indicate that neither manually determined nor program-determined microplots showed a tendency to be wholly included in the other. Therefore, the IOU can be understood to indicate a shifting of microplot boundaries between them.Computation time depends, of course, on the computer used to run the program.The computational times  varied aThe microplot results obtained by Easy MPE were similar to those obtained manually. However, manual identification means only that the accuracy is controlled and judged to be acceptable. Thus, the relationship between the IOU and “absolute accuracy” depends on the precision with which the reference plots are defined. The study results confirm that the accuracy of the program is similar to that obtained by manual identification of microplots, but a direct comparison of the results obtained by the two methods suggests that the program-determined microplots are more precise and more regular than those determined manually .In addition, Easy MPE places a few conditions on the initial image. The field must be fairly homogeneous, and crop rows should not touch each other or not much. These conditions were met by both the soybean and sugarbeet fields in this study, but it is necessary to test other types of fields as well. Continuous field observations (including multiple drone flights) are likely to be required to get the usable image. Image pretreatment by removing weed pixels, either manually or automatically, would help Easy MPE to get good results but is not mandatory.The only manual input to the program is plot delimitation by the user at the very beginning. It is essential that no segmented objects other than the field be included in the region of interest. It might be possible to automate this step by providing GPS coordinates from the seeder or by using an already cropped orthomosaic image, but either method would diminish the freedom of the user to apply the program to an area smaller than the whole field.Also, please note that application of the program is limited by the available computer, as shown by the example of dataset 4.The MPE method used by Easy MPE is different from previously published methods. Easy MPE uses image parameters and information on the field geometry to adapt itself to the field, whereas in the method proposed by Wallhead et al. , a celluThe P-Matrix file, which contains information about the internal and external camera parameters, allowing the 3D coordinates to be converted into 2D coordinatesA DSM file, which is the usual output of SfM softwareAn offset file, which contains the offsets between local coordinate system points used in the creation of some of the output and the output coordinate systemOther published methods do not include the reverse calculation step, which allows Easy MPE to provide images of the same quality as the raw images. The Easy MPE reverse calculation procedure is coded for Pix4D outputs, but outputs of free SfM software outputs could be used as well. Three files are required :The P-MNote that the code would need to be adapted to be able to extract the needed data from free SfM software outputs, but the adapted Easy MPE code would then be entirely free.Other possible improvements include the implementation of a different vegetation index such as NDVI, SAVI, or GNDVI for preprocessing segmentation and the linking of EasyPCC to Easy MPE. Verification methods could also be added as an option; for example, the Hough transform or the average distance between crop rows within microplots could be used to verify that the field components are correctly identified. These methods could be used only with geometric fields that are very constant in the seed repartition; they would thus narrow the current applicability of the code.Finally, the code needs to be further tested and improved by applying it to other important crops that are not as densely planted as soybean and sugarbeet, such as maize or wheat. Fields with crop residues have not been tested either in this demonstration. The impact of a poor quality orthomosaic has not been investigated and should be measured in order to give the plant phenotyping community good insights about the possible uses of Easy MPE.Easy MPE is recommended for its user-friendliness and simplicity; however, many points still have to be tested and approved, which leaves room for improvement. The microplots are delimited in an unbiased way, i.e., without human influence. It is part of an open-source suite of programs designed for the plant phenotyping community, includes a segmentation step if needed, and provides the first automation of the reverse calculation processgrid-based programs are efficient as demonstrated by many publications. Improvement has been made recently, as in , and givin , the proOverall,


In the immediate aftermath following a large-scale release of radioactive material into the environment, it is necessary to determine the spatial distribution of radioactivity quickly. At present, this is conducted by utilizing manned aircraft equipped with large-volume radiation detection systems. Whilst these are capable of mapping large areas quickly, they suffer from a low spatial resolution due to the operating altitude of the aircraft. They are also expensive to deploy and their manned nature means that the operators are still at risk of exposure to potentially harmful ionizing radiation. Previous studies have identified the feasibility of utilizing unmanned aerial systems (UASs) in monitoring radiation in post-disaster environments. However, the majority of these systems suffer from a limited range or are too heavy to be easily integrated into regulatory restrictions that exist on the deployment of UASs worldwide. This study presents a new radiation mapping UAS based on a lightweight (8 kg) fixed-wing unmanned aircraft and tests its suitability to mapping post-disaster radiation in the Chornobyl Exclusion Zone (CEZ). The system is capable of continuous flight for more than 1 h and can resolve small scale changes in dose-rate in high resolution (sub-20 m). It is envisaged that with some minor development, these systems could be utilized to map large areas of hazardous land without exposing a single operator to a harmful dose of ionizing radiation. The area within Ukraine defines the Chornobyl Exclusion Zone (CEZ), which is an access controlled region, established in May 1986, designed to mitigate dose exposure to the public. Restrictions on access are still in place to this day, although tourism permits are currently available as part of official guided tours.The large-scale release of radionuclides from the Chornobyl Nuclear Power Plant (ChNPP) remains the most significant nuclear accident in the history of civil nuclear power generation. During the 10 days of emissions from Reactor 4, approximately 11,780 PBq of radioactive material was released into the environment, including 1,700 PBq of 131I and 85 PBq of In the decades since the accident, there have been significant advancements in remote/automated characterization technologies that were not available to the responding forces in April 1986. Examples of these technologies include radiation-hard robotics systems, stand-off characterization systems and nuclear-focused unmanned aerial systems (UAS). In particular, UAS with radiation mapping capabilities have been shown on a number of occasions to provide excellent results when it comes to mapping radiation within post-disaster environments.There are a number of formats of UASs that have been used to map radiation within the environment, these include helicopter-style systems . The system used within this study showed the capabilities of UASs to operate within even some of the most extreme radiological environments on earth with great effect. The ability of lightweight UASs to fly significantly lower than manned aircraft means that they can achieve improved spatial resolutions and sensitivities despite carrying much lighter  payloads.As well as the heavier systems, like the RMAX presented in Sanada and Torii  (94 kg),Terrestrial radiological measurements acquired from the air require a series of processing steps to convert the collected information into a true estimate of the activity of radioactive material present in the surveyed environment. Over approximately seven decades of operational experience of using manned aircraft, a defined series of processing steps have been established to accurately perform the correction of the raw measurements to a ground activity or dose-rate. This process is termed the “Spectral Windows” method and involves the segregation of the recorded gamma spectrum into a number of discrete energy windows that correspond to particular isotopes. The method is outlined in detail within Minty  and can 238U/235U, 232Th, and 40K) need to be determined regardless of the primary target of the mapping procedure either for direct observation or for correction purposes. However, the characteristic spectral windows span energies in excess of 1.3 Mev , it would be entirely possible to operate a few kilometers away from a survey area. Being able to operate from a distance is a key parameter in responding to nuclear incidents, as it allows operators to be completely or partially removed from a potential hazard. At present, current safety regulations regarding flying beyond visual line of sight (BVLOS) make this type of operation difficult to implement routinely. However, it is possible to obtain exemptions for such flights. It is envisaged that the response to a nuclear incident would constitute a valid exemption to this restriction under a well planned safety case. The work presented within this study presents a new design for a lightweight fixed-wing radiation mapping UAS to monitor terrestrial radiation within post-disaster environments using a modified data processing procedure based on the “Spectral Windows” method. The study aims to demonstrate the advantages of deploying these systems to map relatively large areas of contaminated land in a short time frame within the CEZ  and discThe UAS used within this investigation comprises a fixed-wing vehicle, an integrated radiation mapping payload, and associated ground support equipment. The vehicle was custom built at the University of Bristol based around the “Titan” airframe  . The airThe vehicle featured a full auto-pilot computer with supporting sensors . The autopilot was capable of navigating the aircraft along pre-planned waypoint missions. Three wireless links were used to interact with the vehicle during flight. The pilot safety link, operating on the 433 MHz frequency, was used for initializing the automatic flight and for manual control during the parachute landing. The second link was a bi-directional telemetry modem operating on the 868 MHz frequency and was used for monitoring of flight statistics (such as battery consumption) and to issue updated commands to the autopilot. The third link was a live FPV video stream from a camera in the nose of the aircraft operating on the 2.4 GHz frequency.There are three internal cabins within the body of the “Titan”. The first is the fuselage, which housed the control systems and batteries. The second is the payload bay, which contained the radiation sensor and its associated electronic control systems. The final cabin, located toward the tail of the aircraft, contained the parachute landing system and video transmitter.The radiation sensing payload, supplied by Imitec , followed a similar design to the detection systems utilized within previous works by the authors , meaning that direct and unaided visual contact with the aircraft was required at all times. It is not sufficient to be able to just physically see the aircraft within VLOS restrictions, instead the pilot must be able to ascertain the orientation of the vehicle and be able to avoid other air users and/or obstacles should they be present within the airspace. A number of take-off and land zones were therefore used to split the CEZ into several zones that could maximize coverage within the flight restrictions. No extra modifications were required to operate under these conditions. A minimum separation distance of 1 km from the New Safe Confinement Building (the old Reactor Four site) was also imposed in addition to the standard restrictions.−1 airspeed, although local wind conditions created significant variations in the resulting ground-speed.Each of the flights conducted using the fixed-wing system in the CEZ followed the same operational process. Once a target region was chosen, a small multi-rotor UAS (DJI Phantom 4 Pro) was deployed to investigate the area for obstacles and potential radio interference sources to determine the minimum altitude that the fixed-wing system can safely operate. The minimum altitude was set by adding a margin of 15 m to the measured altitude of the tallest structure present. Wherever possible, operating as low as reasonably practical was the target for every survey. Minor variations in the vegetation canopy height and the land surface throughout the zone resulted in survey altitudes of between 40 and 60 m above ground level (agl). Flight velocities were programmed to operate between 14 and 18 m sOnce the flight altitude was decided, the flight paths were planned in a parallel-line raster scan format. The separation distance between each of the flight lines was equal to the survey altitude in all cases. At this flight line separation, the half-angle of the detectors produce a circle of investigation with a radius equal to the height above ground level of the aircraft Martin, , p. 77. The surveys within the CEZ were completed semi-autonomously. The aircraft was hand-launched in automatic flight mode on take-off and remained in automatic mode until the landing phase of the flight. During the main flight phase, internal parameters  were monitored by the co-pilot using the ground station software. Whenever necessary, certain parameters were altered in-flight by the co-pilot under command from the pilot. Only the landing phase of the flight was conducted manually, with the pilot taking control of the aircraft upon its return to the take-off location. The automatic return to home function was programmed to bring the aircraft directly back to the take-off location whilst climbing to 70 m agl. Due to the lack of appropriate landing strips within the CEZ, the physical landing of the aircraft was accomplished through the deployment of a parachute.TM) and ground-based versions of the fixed-wing mapping system presented in this study. The dosimeters were placed on the body of each of the team of six operators within the CEZ and continuously collected georeferenced dose-rate measurements every 2 s, allowing for aerial measurements to be correlated against ground measurements. The ground-based mapping systems were deployed as handheld devices that were carried by the operators throughout the target areas. Wherever safe and permissible, measurements were recorded by walking in a series of parallel grid lines to ensure optimal coverage throughout the area. More direct routes and quantification pathways were used if the dose-rates were considered high in order to minimize exposure to operators.To further investigate the radiological measurements collected by the UAS, a series of ground-based measurements were collected within areas that were accessible to the ground team over the 6 days of data. These measurements were collected using a series of PED+ personal dosimeters , in μThe raw 10 Hz measurements recorded by the system were first integrated into 1 Hz intervals and corrected for the dead time of the detector during the measurement. Then a correction for the cosmic, aircraft and radon background signal was applied to the data. This correction factor was determined by hovering the detection system at incrementally increasing altitudes over a wide meander bend of the River Pripyat, which is located <20 km from both the Buryakivka and Red Forest survey regions. The water within the river acts as an attenuating medium for the terrestrial signature of the total radiation flux within the surrounding area. With the terrestrial signal removed, the resultant recorded spectrum represents the contribution of all other sources of radiation within the environment. This derived background signal was subtracted from every terrestrial spectrum recorded within the CEZ.137Cs for intervals of a few seconds and visually inspecting the resultant unfiltered and filtered spectra. The energy range of the Sigma-50 detectors used within the payload is reported as 0 - 2 MeV. However the detection efficiency is significantly reduced beyond 1.2 MeV, meaning that observable signals within the potassium and uranium windows are unlikely even after FFT filtering. Hence, spectral stripping cannot be applied. Instead, a third-order polynomial estimation of the baseline of the region surrounding the 137Cs spectral window was calculated and subtracted from the FFT spectrum, removing the Compton background below the peak and leaving only the direct measurements of 137Cs.To keep integration times as short as possible and maximize the achievable spatial resolution of the surveys, a fast Fourier transform (FFT) was applied to the background-corrected spectrum to remove high-frequency noise. The appropriate filtering magnitudes/masks for this task were determined experimentally by exposing the detection systems to a small source of Whilst the aircraft operates at a consistent altitude above the take-off location, the ground surface underneath the aircraft can vary significantly throughout the planned route. As a result, the separation distance between the detector and its target is also varying, which erroneously creates high or low artifacts within the recorded data set. To overcome this issue, the measurements were corrected to a consistent one meter agl using a correction curve derived from hovering the detection system at incrementally increasing altitude overland in the CEZ. This was conducted using a multi-rotor UAS over a fixed-point within a area of land presenting a uniform radiological signature throughout the field of view (FOV) of the detector at the highest altitude within the survey. Spectral information was collected for 90 s at while hovering at each of the 14 altitudes across the range 3 - 150m agl. The resulting spectra were split into four groups, delineated by the energy of the recorded photons , and the relationship between the altitude and energy-specific intensity was determined by fitting an exponential regression line in the form of Equation (1) .I is the measured radiation intensity at altitude, x, agl, I0 is the intensity at ground level (x = 0) and k is an experimentally derived constant encompassing the contributions of geometric dilution and attenuation by the atmosphere between the detector and the source.In this relationship, The height agl of the system was obtained by subtracting the land surface height, obtained from an SRTM 30 m digital elevation model  at a range of distances away from several different sources of 137Cs. In total, two sources were used (labeled RP5 and LRP10), varying in activity between 500 and 1,500 kBq. Background measurements were recorded for both sensors for 30 min and normalized with respect to live time. For the active measurements, both sensors were exposed to the source for 5 min at the same separation distance. The time series data recorded by the dosimeter and the net peak area of the 137Cs peak were extracted and corrected for the background of the laboratory. The conversion factor between the CED and measured 137Cs intensity is represented by the line of best fit of a scatter plot of the two variables , a total distance of 583.8 km was flown by the fixed-wing system, covering a total area of 14.8 kmThe largest average distance traveled in a single flight by the UAS was within the Kopachi region of the map  with a value of 46.3 km. This region is dominated by open fields featuring small amounts of vegetation of <5 m in height, therefore presenting the most optimal conditions for all flight phases. The open fields provided space to take-off and land in any direction and permitted excellent visibility to the aircraft during the in-flight phase, allowing line-of-sight to be maintained easily. The longest distance covered was also in Kopachi, totalling 60.1 km in length. This value describes roughly the total distance that can be covered safely by the UAS given the current battery technology available to operators. If possible, all the flights used within the survey would be closer to this upper limit, but due to line-of-sight restrictions this was not possible in all parts of the surveyed areas.−1, measured at 51.363198 N, 30.107020 E, which is more than 23 times greater than the average total background dose-rate of the UK (0.2 μSv h−1).The results of the derived CED for the Buirakivka survey area are presented within −1, which is 2.8 times greater than the maximum CED recorded within the Buryakivka region. The map shows two main areas displaying elevated dose-rates. The first is a sharply delineated hot spot that extends immediately to the west of the ChNPP itself and covers the “Red Forest” area . The second is a much broader zone of elevated intensity, extending southwards from the plant toward the village of Kopachi . This overall trend is also depicted within the soil sampling investigations previously conducted by Kashparov et al.  that lies within an area of relatively low dose-rate (0.51–1.0 μSv h−1). The hot spot  manifests in an almost idealized point-source geometry when compared to the broad spreading of radioactivity evident within the measurements collected in the area surrounding it. The shape and location of the hot spot suggest that its presence is the result of anthropogenic concentration of radioactivity rather than the natural deposition following the accident. Dose-rate information could not be extracted from the cross-hatched area within Located at the south-eastern corner of the area is a region of elevated dose-rate  of the electronics of the detector. Whilst total count data could still be recorded, the measured gamma spectrum experienced significant degradation when exposed to total count rates of over 5,500 counts per second (cps). This reduction in signal quality manifests in the form of shifting the 137Cs peak toward a lower gamma energy  and significantly increasing the full width at half maximum (FWHM) value. This leads to non-sensical values within the analysis, which have been redacted from the map presented within this study. Detailed analysis of the spectral data indicates that reliable information can be extracted from the detector up to around 5,250 cps, as the peak positions and FWHM values remain within the manufacturers tolerance. Any measurements with a total count rate of greater than 5,250 cps have been removed from the presented dataset.Using the fixed-wing UAS, 15 km−1 . As the detector is facing an overload during these measurements, the real total counts value would most likely be greater, producing a larger CED.Previous surveys have measured dose-rates within a small portion of the “Red Forest” area to be up to around 170 μSv hOverall, the general results from the UAS agrees well with previous datasets collected though other methods. The extensive soil sampling investigations published by Kashparov et al.  provide −1 directly in the vicinity of the “hoppers.” Attempts at recording gamma spectrometry measurements were futile due to saturation issues.Following the identification of the hot spot from the raw data collected by the aircraft, a ground-based team was deployed to investigate the area covered by the elevated intensity region. Upon arrival, this team used SIGMA-50 detectors, Geiger-Muller (GM) tubes and PED+ personal dosimeters to monitor the radioactive output of this region. The source of the radioactivity was determined to be a series of funnel-shaped metal structures that seem to have been used to mechanically sort through material in an attempt to reduce the overall volume of contaminated material following the accident . These s−1 vs. 2 mSv h−1). There may be a number of reasons for the discrepancy between these values. Firstly, the analysis performed on the results collected by the aircraft focuses solely on the 137Cs signal, ignoring contributions from any other radionuclides (these are outside the scope of this study and will be investigated in future studies). The myriad of radioactive material released from the accident is highly complex and the measured contribution of 137Cs is but a component of the total output  that is more easily attenuated by the medium between the source and the detector . The time between the occurrence of the accident and this study is also an important consideration. In the three decades since the accident, radioactive material has had time to penetrate the ground surface and new sediment has had time to be deposited on top of the original radionuclide deposition. The burial of material means that there is not only more attenuating material between the detector and its target source, but the material is also much denser than air, resulting in fewer interactions with unscattered 137Cs photons with the active detection volume.Another reason for the observed disparity between the aerial measurements and the on-ground measurements is the short sampling time and FOV averaging at each point on the earth. As the system operates at a minimum velocity of 14 ms3) and Lanthanum Bromide (LaBr3) detection systems are being considered for future systems as these provide excellent energy resolutions and optical yields, even at small-volumes . This detector has a better tolerance for high gamma fluxes and an improved energy resolution when compared to the Sigma-50 unit used within the current system. It is however, more vibrationally sensitive and will require some efforts to dampen these effects within the UAS payload. A further survey specifically aiming to map the saturated zone of the “Red Forest” see  using th137Cs distribution, although the significant radioactivity of the “Red Forest” proved to be too much for the detectors used within the payload. Whilst this conclusion satisfies the overall aim of this study, there are a few more considerations to be discussed before the system can be considered for use in more routine situations or be implemented into emergency procedures in the future.The results from the radiological investigations of the CEZ suggest that the fixed-wing system presented within this study is effective at mapping −1, but on some occasions ground-speeds of up to 25 ms−1 were recorded during survey lines orientated such that the aircraft experienced a tailwind. As previously mentioned within section 2.3, the differences in wind velocities experienced by the UAS during individual legs of the same survey create inconsistencies in recorded data. The system records raw data at 10 Hz before being resampled in the post-processing phase into 1 Hz intervals. Differences in the velocity of the aircraft mean that the effective sampling area of each measurement varies throughout the survey. As a result, the pixel size has been increased slightly to a lower resolution in the final map to encompass some of this variation.UAS-based investigations are often at the mercy of the weather. Certain counter-measures can be implemented in some cases to overcome problems, for example, waterproofing the central electronic components can allow certain types of UASs to operate even in wet weather. However, the fixed-wing UAS used in this study is sensitive to variations in localized wind velocities. The surveys were conducted at a target velocity 14–18 msEven though UASs with similar or greater ranges have been reported within the literature, the fixed-wing UAS used within this study is considerably lighter than these reported platforms. The Yamaha RMAX platform utilized within Sanada and Torii  weighs 1The concept of utilizing fixed-wing vehicle for this task aims to bridge the gap between manned aircraft and multi-rotor capabilities. Currently, multi-rotor UASs represent the high-resolution end-member of the airborne radiation mapping spectrum, often achieving sub-10 m pixel sizes , whereas a manned survey would be considerably more expensive. The cost for repeat surveys following the initial investment totals at $9,000 for the equivalent survey conducted within this study. This is based on salary estimates and operational costs for a three man crew over 6 days of active operation. If the equipment is used multiple times, the cost-benefit of the system is significantly improved over utilizing manned aircraft.Without much prior familiarity of operating within the CEZ, the fixed-wing system was successfully deployed at as low an altitude as reasonably possible using information obtained from on the fly pre-flight surveys. With a good knowledge of the survey area, it would be possible to achieve much more. Overall, the authors believe that there is extreme promise in widely utilizing these systems for a number of survey applications in the future after the implementation of the improvements suggested herein.2 was investigated in a high spatial resolution (20 m pixel−1). In total, more than 580 km were flown across the region in a total flight time of 09h:17m:37s. The system demonstrated that radiation mapping investigations using UASs can be launched from safe-zones outside contaminated regions and operated continuously for more than an hour before returning to the safe-zone to land. Some previous systems presented within the literature have been required to launch within the contaminated zones at the risk of the operators.This study presents the most comprehensive radiation map of the CEZ ever produced from a UAS. Over the 6 days of active fieldwork with the fixed-wing system, 15 kmDue to issues with detector saturation, the area in the most radiologically intense portions of the “Red Forest” were not presented with the mapped CED as this information could not be reliably extracted from the spectra recorded over this area. This problem is hoped to be solved in the future by operating using a different dual detector set-up (Sigma-50 and GR1 combination). In this configuration, the Sigma-50 would be used to map the areas displaying lower contamination concentrations (as presented by more than 85% of the area mapped in this study) and the GR1 would be used to map the areas wherein the Sigma-50 was saturated.−1 hot spot exists, a coordinated ground sampling investigation will be conducted to determine the nature of the radionuclide content and correlated against the measurements collected by the aerial platform.One of the most interesting findings was the presence of the previously unreported, anthropogenically-enhanced hot spot located in the south-eastern corner of the surveyed area. With the knowledge that the 2 mSv hThe work conducted within the CEZ was part of a multi-faceted field investigation using numerous types of radiation monitoring methods. These included both fixed-wing and low-altitude multi-rotor UAS surveys, as well as ground-based monitoring methods using both tracked robots and humans. The data presented herein will be combined with the measurements recorded using the other methods in future works to complete a comprehensive radiological survey of the CEZ using mobile radiation monitoring methods. The demonstration of this system in this environment has further-reaching consequences than just the monitoring of post-disaster environments. With alterations to the included detection systems, using recommendations from Lowdon et al. , this syThe datasets generated for this study are available on request to the corresponding author.Data collection was conducted by DC, KW, PM, YV, SG, IC, and TS. Continual aircraft development was conducted by KW and TR, with the piloting of the aircraft performed by KW. Data processing was conducted by DC with assistance from KW and DM-S, with necessary local information provided by IC and SK. Original manuscript preparation was conducted by DC. Further manuscript developments and improvements were performed by DC, KW, PM, TS, DM-S, SG, IC, TR, SK, and NS.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


French bulldogs exhibit significantly larger femoral external rotation and abduction than other breeds. We were curious as to whether this peculiar leg kinematic affects patellar motion and/or might induce medial patellar subluxation (MPSL) or medial patellar permanent luxation (MPPL). We hypothesized that the more abducted leg posture during stance causes an unusual medial pull direction of the rectus femoris muscle during stance, and that this may facilitate the occurrence of MPSL or even MPPL during locomotion. To test our hypothesis, we analyzed existing stifle-joint X-ray-sequences collected during the treadmill walk and trot of seven adult female French bulldogs. We estimated 3D-patellar kinematics using Scientific Rotoscoping.The three-dimensional motion of the patella comprises rotations and translations. From the seven dogs analyzed, three exhibited MPSL and one MPPL during the gait cycle. Medial patellar luxation (MPL) occurred mostly around toe-off in both gaits studied.p = 0.037) at touch-down (TD) and at midswing (p = 0.024), and significantly more medial at midswing (p = 0.045) compared to dogs without MPL.Patellar position was generally not gait-related at the analyzed timepoints. In dogs with MPL, the patella was placed significantly more distally (Medial patellar luxation seems to be the consequence of the far from parasagittal position of the stifle joint during stance due to a broad trunk, and a wide pelvis. This peculiar leg orientation leads to a medial sideway pull caused by the rectus femoris muscle and the quadriceps femoris and may initiate plastic deformation of the growing femur and tibia. Thus, a way to avoid MPL could be to control breeding by selecting dogs with lean bodies and narrow pelvis. Actual breeding control programs based on the orthopedic examination are susceptible to errors. Systematic errors arise from the fact that the grading system is highly dependent on the dog’s condition and the veterinarians’ ability to perform the palpation on the stifle. Based on our results, the position of the patella at TD, or even perhaps during stand might offer a possibility of an objective radioscopic diagnostic of the MPL.The online version contains supplementary material available at 10.1186/s12917-021-02787-z. InterTwo-dimensional patellar kinematics in the dog showed that the motion of the patella relative to the femur is coupled with the stifle flexion-extension and task related . In addiStylianou and colleagues used a cadaveric pelvic limb of an adult dog to analyze stifle joint (including patellar) kinematics and forces. In their experiment, the authors displaced the pelvis vertically 75 mm, producing a flexion of the stifle. Such a vertical excursion exceeds by far those we estimated in our scientific rotoscoping studies at walk or trot . A comparison is further hampered because the authors presented their results in a way that makes quite difficult to read the amplitude of the motions .Medial patellar luxation occurs mostly around TO in both gaits studied. In dogs with MPSL, the patella moves medially approx. 35% of its width. This value increased to more than 60% in the dog with MPPL. Dogs with MPL exhibited a more distally placed position of the patella at TD, and sometimes reduced vertical motion. During walk, dogs displayed similar patellar patterns as in trot, however, the amplitude of the motions was lower. Dogs exhibited the same MPL grade as in trot.It has been suggested that MPSL is related to tibial torsion (known as the “screw-home” mechanism) during the early swing phase , 24. We ligamentum patellae has been shortened, or that the tendon of the quadriceps femoris muscle has been elongated, for which the last-mentioned idea seems more plausible of both.The more distally placed position of the patella at TD found in dogs with MPL could indicate that either the A hypothesis to be tested is that MPL relates to a broad trunk, a wide pelvis, and hence the distance between feet at touch-down or the width of foot trajectories. The greater the width the more abducted and externally rotated will be the limb kinematics. Body weight, or better overweight, might have a crucial impact on this. The quadriceps femoris muscle as anti-gravity muscle will pull on the patella in an oblique direction to the femur, especially after midstance. Depending on the relative distance between midpoint of the body and touch-down point, this angulation results in medial pull, as well as on the patella and secondary rolling of the femur. Muscle forces then by time force the patella out of the sulcus, resulting in PL. Whether this relates in any way to the 12 times more common MPL in small dogs remains open.Knowledge of patellar kinematics and dynamics may open alternative pathways for a better understanding of the pathogenesis of PL and open new possibilities for classifying. The impact on surgical treatment, however, remains open and necessitates further clinical research.Regarding pathogenesis, our findings place the rolling mechanism of the femur in a central role, attributing it an initiating mechanism to many other and well described accompanying findings. By opening a wider anatomical and biomechanical frame for the understanding of MPL, the pathogenetic explanation starts with a broad and tilting pelvis, which leads to a rolling and abduction of the femur in the stance phase, not allowing the patella to follow its movement because it is held in position by the fixed lower hind limb. As these peculiar movement pattern already starts early in the life of the dog, the weak bones of the growing animal must undergo plastic deformation by strong muscle pull. These plastic deformations however are well known, summarized as malalignment of the pelvic limb and specifically described as coxa vara, genu varum, distal femora varisation, proximal tibial valgisation, torsional deformity of the near stifle region and shallowing of the femoral trochlear groove , 13, 38.Up to now, breeding control programs were based on the orthopedic examination, and the grading system established in the late 1960s , 17. HowAn alternative way to avoid MPL could be to control breeding by selecting dogs with lean bodies and narrow pelvis. Support for this theory comes from a historical point of view. The patella is nowadays considered as a sesamoid bone, but as a remnant of an apophysis at the distal femur. The rectus femoris muscle found its insertion there, leaving the patellar ligament a quite stiff cranial stifle stabilizer. Other parts of the quadriceps muscle ended in the medial and lateral fascia inserting at the pes anserinus on the medial tibia and on the fascia of the cranial tibial muscle. Detaching the apophysis from the femur resulted in free moveable bone, the patella, which then came under the influence of laterally and medially located muscles, which controlled adduction and abduction. If the medial vastus muscle now pulls towards medial at the same time as the femur abducts, as in the case of broad stance or high body weight, the patella luxates.Medial patellar luxation occurs mostly around TO in both gaits studied. It seems to be the consequence of the far from parasagittal position of the stifle joint during stance. This peculiar leg orientation leads to a medial sideway pull caused by the rectus femoris muscle of the quadriceps femoris. Thus, the pelvis conformation is a possible indicator for a MPL. Further research on the form of the pelvis, pelvic width, and acetabular retroversion is required to support this theory, and to establish a possible new standard for breeding control programs. It must be noted, that even when correlation or the clinical occurrence of MPL could be found, the trigger to MPL could be somewhere else, and pelvis conformation is only the result of a different or remote trigger. Finally, our results showed that the position of the patella at TD, or even perhaps during stand might offer the possibility of radioscopic diagnostic of the MPL.We selected seven adult female French bulldogs data from a pool of dogs who participated to earlier studies . Blue, and red dots indicate that those axes point into the picture. Figure S2. The zero position of the patella was set as follows: A sphere was centered in the middle of the patellofemoral joint  and z ]. Green, and red dots indicate that those axes point into the picture. Figure S4. The vertical crest of the facies articularis of the patella was moved until it touched the sphere (compared to Figures S


Wi-Fi fingerprinting positioning systems have been deployed for a long time in location-based services for indoor environments. Combining mobile crowdsensing and Wi-Fi fingerprinting systems could reduce the high cost of collecting the necessary data, enabling the deployment of the resulting system for outdoor positioning in areas with dense Wi-Fi coverage. In this paper, we present the results attained in the design and evaluation of an urban fingerprinting positioning system based on crowdsensed Wi-Fi measurements. We first assess the quality of the collected measurements, highlighting the influence of received signal strength on data collection. We then evaluate the proposed system by comparing the influence of the crowdsensed fingerprints on the overall positioning accuracy for different scenarios. This evaluation helps gain valuable insight into the design and deployment of urban Wi-Fi positioning systems while also allowing the proposed system to match GPS-like accuracy in similar conditions. Additionally, their permanent connection to the Internet using mobile data or a Wi-Fi connection has allowed the use of mobile phone sensors in areas such as transportation, tourism, logistics, air quality monitoring, and social networking ,2. MobilWe consider that the development of the mobile crowdsensing paradigm is essential to the development of functional Wi-Fi fingerprinting positioning systems for urban and outdoor areas. Previous research on fingerprinting positioning is mainly aimed at designing techniques and systems for indoor areas ,4.While the development of indoor location-based services and positioning systems have been motivated by complex, non-line-of-sight (NLOS) environments and the general unavailability of Global Positioning System (GPS) localization, urban crowdsensed fingerprinting positioning will compete in offering accurate positioning with the Global Navigation Satellite System (GNSS) for mobile devices equipped with both types of sensors and will prove to be a problem-solver for energy- and size-constrained IoT devices requiring GPS-like accurate positioning.gps.gov website, states that GPS has an average positioning accuracy of 5 m in rural areas and approximately 16.8 m in urban environments. This sets a clear accuracy goal for Wi-Fi positioning systems designed for urban environments, consisting of attaining similar or better accuracy with lower costs.Reference , cited oEarly research on urban wireless positioning aimed at improving location-sensing accuracy by eliminating typical non-line-of-sight errors while not acknowledging the benefits of applying crowdsensing in enabling a Wi-Fi urban positioning system ,7,8.Other technologies that are readily available in smartphone devices used for crowdsensing, such as Bluetooth , cellulaIn this paper, we apply mobile crowdsensing to prove the feasibility of designing a Wi-Fi fingerprinting positioning system for urban areas. The envisioned system benefits from the ubiquitous presence of Wi-Fi access points (APs) in urban areas, as proven in our previous research ,15,16. TContinuous growth and resolution of the coverage area for the positioning systems;Coverage of areas restricted from public access ;Constant feedback on the APs situation, allowing for the removal of inactive, mobile, or moved APs;A high density of fingerprints in areas most frequented by users, resulting in a higher accuracy of the positioning in areas most likely to be required.We propose the usage of the crowdsensing technique as opposed to previous research using automated or grid-based fingerprint collection for indoor positioning systems. Finally, the envisioned positioning system will require continuous feedback from users that choose to contribute to the crowdsensing effort. This will enable:Regarding the aspect of using different smartphones, our research was not aimed at mitigating the effects of distinct hardware and software, as this would prove to be an impossible task for a crowdsensing system. This is caused by the numerous smartphone devices available on the market that would ultimately be able to contribute to the proposed system.The main challenge specific to the proposed solution lies in proving that fingerprinting positioning can achieve GPS-like accuracy in similar conditions specific to the urban environment. To address this we thoroughly evaluated the accuracy of the fingerprinting positioning system using different sub-datasets extracted from our collected data. For the position estimation, we apply the methods proposed in the papers published by Lohan et al. and Mendoza et al. ,18.The remainder of the article is organized as follows: Outdoor positioning is usually based on multilateration or triangulation techniques  which woWi-Fi fingerprinting has traditionally exploited Wi-Fi interfaces equipped on mobile devices, not limited to smartphones , as wellDesigning a fingerprinting positioning system for urban environments requires knowledge of the presence and density of Wi-Fi APs in the system’s coverage area. Our previous work on fingerprinting positioning  proved tAchieving seamless urban positioning with no constraints on the indoor/outdoor environment conditions is a significant step towards implementing a global indoor positioning system, as proposed in .As highlighted in previous research ,18, Wi-FThe data collected for our research consisted of more than 1 million Wi-Fi measurements collected in Bucharest (Romania) and areas nearby the city by the help of eight different smartphones carried by different users for 6 months. The data collection process was a part of the everyday life of users and they were not required to scan in a specific way, space, or time. This means that the collected measurements were collected while stationary or when the users were moving from one place to another by any means available in the city.Due to the crowdsensing method, the collected measurements are denser near the city center where the scanning occurred more often. This prompted us to select one of the most frequented areas, shown in The chosen test area has a high density of Wi-Fi measurements and allows for long-term variations in Wi-Fi signal propagation. The area is characteristic to the urban environment of Romania, being placed between two high-traffic boulevards while the buildings in the area are tower apartment blocks. Locations of measurements in the test area are shown in The test area, with a surface of 1 kmx and y axes.The fingerprint coordinates were transformed from the decimal degree format to local coordinates. The decimal degree format is used to express latitude and longitude geographic coordinates as decimal fractions, while the local coordinates are referenced to the upper-left corner of the test area and are expressed in meters on the The test area dataset and all subsequent sub-datasets were randomly split between training data  and test data (15% of the remaining fingerprints). The training fingerprints were used for estimating the position of the test fingerprints. The resulting estimated position was compared to the test fingerprint position resulting in a positioning error. Fingerprint collection by the crowdsensing technique has implications for the density and the received signal strength characteristics.The density of fingerprints influences the accuracy of the crowdsensed positioning system and is closely linked to the number of people that have visited a certain area. The average signal level (dBm) of the fingerprints in the test area is shown in The placement of the AP, for example, the top of a building, and the crowdsensing technique influence the signal strength at ground level where the measurements were collected. APs placed on lower floors or at ground level have the chance to be scanned more often and be seen with a higher signal level, as shown in APs in densely scanned areas are themselves more likely to be scanned more often;The crowdsensing method offers consistent results for successive scans of the same area;APs’ radio visibility displays a character of stability for repeated scans.Access points scanned less than three times can increase the positioning error significantly, as they are most likely placed at a great distance from the measurement points or are outside the test area.The metric that used for the evaluation of the positioning accuracy of the crowdsensed data was the mean 2D error. The error was calculated as the Euclidean distance between the estimated position and the GPS-determined positions of the test measurements. The distances are expressed in meters using the local coordinates described previously.As mentioned, the evaluation of the positioning accuracy in an urban test environment for the crowdsensed data was done using the weighted-centroid method ,37 and tThe weighted-centroid method was proposed for fingerprinting positioning in reference  as a simUsing the set of visible APs as Weighting is done according to the distance to the APs’ positions. Since the RSS can be used as a distance function to the APs, the weights can be replaced by the RSS, using the measurement vector Since our proposed technique is aimed at using crowdsensed measurements, the APs’ positions will not be known. Thus, the position of all measured APs was estimated as a first step using the weighted-centroid approach, similarly to the above steps.p and r, defined as: The log-Gaussian distance method used in our paper was applied similarly as proposed in . The pos30.017 m for the weighted centroid method;45.414 m for the log-Gaussian distance method.The initial results for both algorithms when applied to the complete test area fingerprint database showed the following mean 2D error:These mean error values are significantly higher than those reported in previous studies where the test area had a smaller surface and was placed indoors, for example, approximately 10 m in  or approThe mean error of outdoor fingerprinting positioning systems can be reduced using database processing, as proposed in ,42,43, wEach method has its characteristics which influence the resulting positioning error and also the way they benefit from the crowdsensed fingerprints. The implementation of the weighted-centroid method requires an initial step preceding the actual positioning that requires the estimation of the position of all the APs in the fingerprint database. Using crowdsensed data for AP position estimation can easily lead to errors in estimating the AP’s exact location. This is to be expected, as crowdsensed fingerprints are collected alongside roads or near buildings. The initial step mandates the study of the influence number of crowdsensed fingerprints for each AP.k-nearest neighbor (k-NN) approach on the k most similar fingerprints. For the evaluation of the method, the number of neighbors was set to k may lead to an estimated position further away from the test position due to the inclusion of less-similar fingerprints that were collected at a greater distance. This can be solved by applying a weight to the k-NN algorithm or by evaluating the influence of the crowdsensed data on the position estimation algorithm.The log-Gaussian method aims at finding the most similar fingerprints with the measurement vector used for positioning by the help of the log-Gaussian distance. The position estimation is then done using the The influence of mobile APs;The average number of fingerprints for each AP;The signal level of each AP measurement included in a fingerprint;The average signal level of the APs;The estimated coverage radius of the APs;k-NN estimation.The number of neighbors used for the The particularities of each positioning method coupled with the less-known influence of the crowdsensed fingerprints prompted us to research the following aspects which influence the accuracy of crowdsensed fingerprinting positioning:Mobile APs can lead to significant positioning errors. For example, an AP installed in a public transport vehicle could lead to positioning estimation results placed in the part of the town where the AP was scanned. This prompted us to apply a method for removing the fingerprints of mobile APs, while also investigating the effects of their presence on the overall positioning performance.The identification of mobile APs requires the analysis of the whole fingerprint database, not only of the test area. Mobile APs are classified using a method previously published by the authors in .The method is based on the assumption that APs maximum coverage radius is rarely larger than 300 m. All measurement points for a distinct AP are selected and used to create a cluster. The minimum bounding box and the centroid point of the cluster are calculated. The maximum coverage of the AP is determined to be as the maximum distance between the centroid point and the minimum bounding box. Thus, APs with a range greater than 300 m are considered mobile.3506 APs representing 34.8% of the total were scanned once and could not be classified as fixed or mobile. Thus they were eliminated from the comparative dataset;395 APs representing 3.9% of the total were classified as mobile and were eliminated from the comparative dataset.By applying the described method we attained the following results:The average number of fingerprints per AP could influence both positioning methods when considering crowdsensing as the collection method. A low number of fingerprints can limit the information available in the AP position estimation step characteristic to the weighted-centroid method while a large number of fingerprints correlated to a large coverage area might reduce the unique character of each fingerprint, leading to an increase in the positioning error for both methods.Reduced fingerprint dataset containing APs averaging 1 to 5 fingerprints resulting in 13,647 fingerprints of 6798 APs;Average fingerprint dataset containing APs averaging 6 to 20 fingerprints resulting in 24,890 fingerprints of 2318 APs;Dense fingerprint dataset containing APs averaging 21 to 60 fingerprints resulting in 26,957 fingerprints of 828 APs;Very dense fingerprint dataset containing APs averaging more than 61 fingerprints resulting in 10,234 fingerprints of 128 APs.To study the previous hypothesis, the dataset was split into subsets using the average fingerprint number for each AP as the criteria as follows:The resulting mean positioning error for each sub-dataset is shown in The weighted centroid displayed increased precision for the reduced fingerprint dataset. This is caused by a higher positioning accuracy in scenarios where a reduced number of fingerprints were collected for the same APs in close proximity to each other. This is the case for APs that are visible in a narrow area at street level due to their placement in a distant position. For the datasets with a higher fingerprint-to-AP ratio, the precision was lower, mainly due to a decrease in the AP position estimation caused by the higher number and wider geographical distribution of the APs’ fingerprints.The log-Gaussian method had a worse result for the reduced and very-dense datasets and significantly better accuracy for the intermediate values. A low number of fingerprints for APs means that less data is available for the statistic characterization of the data, while a large number of fingerprints often leads to the phenomena of geographic diffusion of precision where similar signal levels are found in fingerprints placed far away from each other.Very dense fingerprint-to-AP ratios led to a decrease in positioning accuracy, leading to the conclusion that collection and storage of large volumes of fingerprints is not always beneficial to the fingerprint positioning system.The signal level’s influence on positioning accuracy was studied by splitting the dataset into subsets using the signal level value expressed using dBm units as a threshold. The subset split aimed at keeping the number of fingerprints in each subset similar. The results of the analysis are displayed in As shown in The average AP signal level was calculated as the average level of all collected fingerprints for each distinct AP. Again, the dataset was split into subsets using the average signal level as the criteria, aiming at keeping the number of fingerprints in each set similar. The results of the analysis are displayed in As fewer APs were included in each subset due to the filtering, the weighted-centroid method performed worse in each scenario. The log-Gaussian method had a minor accuracy improvement for APs with an average signal level close to the average signal level of all collected fingerprints.X in a Euclidean plane or space is the smallest convex set that contains x. When applying the convex hull operation to the set of points consisting of the positions of all fingerprints collected for an AP, the result will be an estimation of the area covered by the AP.The AP coverage radius was estimated by the help of the crowdsensing process. The scanning process continued for five months inside the test area, allowing us to assume that street-level coverage was captured with precision. The average coverage radius of APs in the test area was approximately 47 m. The coverage area was determined by the help of the convex hull operation. The convex hull or convex envelope of a set of points The dataset was split into subsets using the estimated coverage radius as the criteria, and the results of the accuracy analysis are displayed in Null radius—6506 fingerprints of 4988 APs;Radius between 0 and 30 m—17,618 fingerprints of 2634 APs;Radius between 30 and 50 m—19,603 fingerprints of 1328 APs;Radius between 50 and 80 m—18,659 fingerprints of 774 APs;Radius between 80 and 110 m—8074 fingerprints of 250 APs;Radius between 110 and 170 m—3665 fingerprints of 81 APs;Radius larger than 110 m—4728 fingerprints of 98 APs;The resulting subsets based on the coverage radius had the following dimensions:The positioning performance of the weighted-centroid method benefited from the usage of APs with an estimated coverage radius under 30 m, due to its initial step which requires the estimation of the AP position. A lower-radius AP is less likely to be positioned erroneously.The log-Gaussian method had significantly greater accuracy for APs with an estimated radius under 50 m. For both methods, an AP coverage radius larger than 80 m resulted in larger positioning errors related to the geographic dilution of precision phenomena.This analysis allows us to gain insight that can help set an upper limit on the estimated AP coverage area which in turn can limit the volume and effort related to the crowdsensing process.As previous filtering shows mixed results for the compared positioning methods, we highlight the results of the weighted-centroid and log-Gaussian methods in Similarly to the weighted-centroid method, as As can be seen in both tables, the methods had opposite performances for the same fingerprint subsets, except for the one with APs having a radius between 0 and 30 m. This results in the opportunity of using multiple estimation methods for the envisioned urban fingerprinting positioning systems that can be applied to different areas to be covered by the system. For example, if computed AP statistics show that an area is scanned more densely during the crowdsensing process, the log-Gaussian method should be used, as it outperformed the weighted-centroid method (38.080 m vs. 47.490 m average 2D error). Otherwise, if an area is characterized by a low average number of fingerprints for each AP the weighted-centroid method will outperform the log-Gaussian method (26.916 m vs. 91.198 m average 2D error).k-NN method is often used for position estimation by fingerprinting systems. The k-NN method was combined with the log-Gaussian distance to select the most similar neighbors in the fingerprint space. Previous papers have proposed different values for k, according to the environment that the fingerprinting positioning system was to cover. References [k = 3 or 4 for indoor fingerprinting systems.The ferences ,44 propoThe analysis of the influence of the number of neighbors on the accuracy of the positioning was done by varying the number between 1 and 10 and by computing the average 2D positioning error in each scenario. The dataset used contains 15,002 fingerprints of 1929 APs in the test area with an average estimated radius between 0 and 30 m. The results are displayed in According to Our analysis shows that the positioning accuracy of outdoor fingerprinting systems can be significantly improved by filtering and optimizing the contents of the fingerprint database. The best results came close to an average 2D positioning error of 21 m for the log-Gaussian distance method. These values do not come close to that achieved by previously published research on indoor systems. Nevertheless, our results show that urban fingerprinting positioning can reach GPS-like accuracy in similar use-cases as reported by reference .The accuracy of crowdsensed fingerprinting positioning systems is shown to be dependent on the density of fingerprints, quantified as the average number of fingerprints collected for each AP. As the average positioning error was computed for the whole test area, including areas that were scanned less often and dense, it is safe to assume that for some of the most-scanned roads inside the test area the precision of the fingerprinting system would outperform that of GPS.Wi-Fi fingerprint positioning systems for urban environments have become an intense topic of research in the context of Internet-of-Things technology. These positioning systems have the ability to offer GPS-like positioning accuracy for energy-constrained devices equipped with a Wi-Fi or 5G radio network interface that is used both for communications and positioning.To evaluate the performance of urban fingerprint positioning systems, we built our fingerprint database using the crowdsensing technique for data gathering. Since the crowdsensing approach makes it difficult to cover a whole city with a small number of contributors, we chose to conduct our analysis on a dedicated test area specific to the Bucharest urban environment.k-NN method.We analyzed the crowdsensed fingerprint and highlighted important aspects related to the research problem, including the presence of mobile APs, the influence of the average number of fingerprints collected per AP, the fingerprint signal level, the average street-level AP signal level, the influence of the estimated AP coverage radius, and the performance of the We showed that the chosen positioning methods mostly performed in different ways when particular filters were applied. For example, the weighted-centroid method had a 17.922 m average 2D error for the subset with fingerprints had signal levels between −71 and −75 dBm, while the log-Gaussian method offered poor results at an average 2D error of 122.574 m. The main differences in the methods’ performances can be assigned to the fact that the weighted-centroid method requires an initial estimation of all of the APs’ positions, while the log-Gaussian method uses a similarity function that can perform poorly in particular situations, as proven by the fingerprint filtering.We conclude by proving that fingerprint positioning systems can achieve or even out-perform satellite positioning systems in an urban environment, mostly due to the density of Wi-Fi APs and the difficulties that other positioning systems have in these conditions.


Botulinum toxin type-A (BTX-A) injection for treating chronic migraine (CM) has developed into a new technique covering distinct injection points in the head and neck regions. The postulated analgesic mechanism implies that the injection should be administered to sensory nerves rather than to muscles. This study aimed to determine the topographical site of the auriculotemporal nerve (ATN) and to propose the effective injection points for treating CM. ATNs were investigated on 36 sides of 25 Korean cadavers. The anatomical structures of the ATN were investigated focusing on the temporal region. A right-angle ruler was positioned based on two clearly identifiable orthogonal reference lines based on the canthus and tragus as landmarks, and photographs were taken. The ATN appeared superficially in the anterosuperior region of the tragus. The nerve is located deeper than the superficial temporal artery. And it runs between the artery and the superficial temporal vein. In the superficial layer, it is divided into anterior and posterior divisions. The anterior division runs in a superior direction, while the posterior division runs in front of the ear and the several branches are distributed to the skin. We suggest that the optimal BTX-A injection points for CM are in the temporal region. The first point is about 2 cm anterior and 3 cm superior to two orthogonal reference lines defined based on the tragus and canthus, and the second point is about 4 cm superior to the first point. The third and fourth points are recommended about 2 cm superior to the first point, but respectively 1 cm anterior and posterior to it.  SeveralBTX-A injections for treating chronic migraine (CM) has developed into a new technique covering distinct injection points in the head and neck regions . That stThe optimal intervention could be elucidated by focused observations of the nerve distribution in the target area based on consistent anatomical landmarks. The present study was therefore designed to determine the topographical site of the auriculotemporal nerve (ATN) as it exits from the deep layer to the superficial layer, and to propose the effective injection points for treating CM.The ATN appeared superficially in the anterosuperior region of the tragus. In that region, the nerve is positioned deeper than the superficial temporal artery and passes between the artery and the superficial temporal vein .In the superficial layer, it is divided into anterior and posterior divisions. The anterior division runs in a superior direction, while the posterior division runs in front of the ear and the several branches are distributed to the skin. The area in which the distribution of the nerve was greatest is presented in Nerve block procedures have been used in headache conditions as well as in neuropathic pain. Despite the limited evidence base, previous studies reported that the peripheral nerve block procedures including occipital, supraorbital, supratrochlear, and ATN can be effective in primary headaches . ATN is Baumel 1971) reported on the anatomy of the ATN [71 reportPrevious studies have found that injecting BTX-A results in it diffusing over an area with an average diameter of 6–10.9 mm ,21,22. WBTX-A is the only prophylactic therapy that has been approved specifically for CM. The first clinical observations of the possible beneficial effects of BTX-A on headache were made when the toxin was applied to patients for cosmetic reasons. This has resulted in the nomenclature of the injection sites being based on muscles. BTX-A injections for CM have evolved into a standardized technique covering at least 31 distinct injection sites across the head and neck regions . An examIn contrast, Ashkenazi and Blumenfeld (2013) suggested injection points for CM patients based on the consideration of the distribution of sensory nerves . These cIn this study, we proposed the injection points into temporal region, based on the topographic anatomy of the ATN. However, we could not clarify whether the temporal fascia plays in role as a barrier of the BTX-A diffusion. A further study is needed about the diffusion and migration of BTX-A.How BTX-A affects CM is not fully understood. However, the roles of BTX-A in atypical pain processing, central sensitization, cortical hyperexcitability, and neurogenic inflammation have been studied. BTX-A directly inhibits peripheral sensitization by attenuating the exocytosis of neuropeptides and neurotransmitters from peripheral sensory neurons, thereby indirectly reducing central sensitization . The relThe analgesic effects exerted by BTX-A via effects on sensory neurons as discussed above indicate that injections of this agent should target sensory nerves in the trigeminal–occipital–cervical complex. The ATN is the main source of innervation in the temporal area. BTX-A should be injected superficially into the temporal area in the ATN distribution area, under guidance based on clearly identifiable and consistent anatomical landmarks and a reference axis. We suggest that the optimal BTX-A injection points for CM are in the temporal region. The first point is about 2 cm anterior and 3 cm superior to the two orthogonal reference lines based on the canthus and tragus, the second point is about 4 cm superior to the first point, and the third and fourth points are both about 2 cm superior to the first point, but respectively 1 cm anterior and posterior to it. Because the ATN travels superficially over the temporalis muscle, the agent needs to be injected subcutaneously.This study investigated ATNs on 36 hemifaces of 25 Korean cadavers ; 14 specimens were excluded due to loss during dissection. The 25 cadavers were embalmed with a fluid containing 10% phenol/formaldehyde. After removing the skin and subcutaneous tissues of the upper and lower faces, the anatomical structures of the ATN were investigated, focusing on the temporal region. The superficial temporal fascia was carefully reflected so that the ATN distribution layers could be discerned. Each specimen was dissected in detail while taking extreme care not to damage the main structures. After dissection, the canthus and tragus were set as landmarks to establish a horizontal reference line. A vertical reference line was then set at the front of the tragus, perpendicularly to this horizontal line. A right-angle ruler was positioned along these reference lines, and photographs were taken. The obtained photographs were calibrated to the same scale using computer graphics software . All photographs were adjusted to the right side, and ATNs were traced on each specimen. The photographs were then superimposed on a layer-by-layer basis, and the area with the greatest nerve distribution was marked.