Three designs for electrodynamic flexural transducers (EDFT) for air-coupled ultrasonics are presented and compared. An all-metal housing was used for robustness, which makes the designs more suitable for industrial applications. The housing is designed such that there is a thin metal plate at the front, with a fundamental flexural vibration mode at ∼50 kHz. By using a flexural resonance mode, good coupling to the load medium was achieved without the use of matching layers. The front radiating plate is actuated electrodynamically by a spiral coil inside the transducer, which produces an induced magnetic field when an AC current is applied to it. The transducers operate without the use of piezoelectric materials, which can simplify manufacturing and prolong the lifetime of the transducers, as well as open up possibilities for high-temperature applications. The results show that different designs perform best for the generation and reception of ultrasound. All three designs produced large acoustic pressure outputs, with a recorded sound pressure level (SPL) above 120 dB at a 40 cm distance from the highest output transducer. The sensitivity of the transducers was low, however, with single shot signal-to-noise ratio  Air-coupled ultrasonics is of great interest for many applications, including wireless communication , contactOther available air-coupled transducers include capacitive micromachined ultrasound transducers (CMUTs) ) and pieAn alternative solution is to use flexural transducers , which uHowever, flexural transducers are not without drawbacks. The bonding between the piezoelectric ceramic and the plate may degrade or even disbond over time, particularly when subjected to a hostile environment . Moreover, bonding a piezoelectric element onto a plate inevitably alters the mode shapes and frequencies of the flexing plate, and the manufacturing process of conventional flexural transducers makes it difficult to produce coherent transducers with overlapping bandwidths.In the NDE community, electromagnetic acoustic transducers (EMATs)  are routIn this paper, we combine the working principles of flexural transducers with EMATs. Three electromagnetically actuated flexural transducers  are proposed and tested. Compared to conventional flexural transducers, EDFTs generate vibrations directly on the metal plate through electromagnetic coupling, which simplifies the manufacturing process of the transducers and offers an improved robustness for high temperature applications. Also, better consistency in the performance of the transducers can be achieved due to the elimination of the bond layer. Because of the greater flexibility in the design of the excitation coil, novel transducer geometries are made possible.The EDFTs investigated here are shown schematically in One drawback of the EDFT is a loss of electro-mechanical efficiency compared to piezoelectrically actuated transducers, as well as the requirement of a high amplitude pulsed current, which can be an inconvenience for intrinsic safety requirements. However, for some applications, these considerations are of less importance, and the advantages makes the EDFT a relevant addition to air-coupled ultrasonic technology.The electrodynamic coupling to the front face of the EDFT is via the Lorentz mechanism in this case, as is often the dominant coupling mechanism with EMATs. Generally speaking, three mechanisms are responsible for the electro-acoustic transduction of EMATs: the Lorentz force, the magnetostrictive force, and the magnetization force . An EMATIn the detection process, when the atoms and free electrons in the metal move due to the displacement associated with an ultrasonic wave, the free electrons with energy above the Fermi level will experience a Lorentz force in the presence of a magnetic field. These electrons constitute a current that will generate a magnetic field, which when within the electromagnetic skin depth of the surface will produce a magnetic field external to the sample that can induce an electromotive force (voltage) on a suitably oriented detection coil . An EMATWhile the operational principles of the EDFT device proposed in this paper are similar to those of an EMAT, there are some subtle differences—mainly the dilation characteristics of the flexural membrane and how they affect the induced magnetic/electric fields. A complete understanding of this physical phenomenon warrants a detailed investigation, and will be reported at a later date. The main purpose of this paper is to perform an empirical study on three selected proof-of-concept EDFT device configurations.A Polytec laser vibrometer  was used to measure the front face displacement of the transducers, and an acoustic microphone  to measure the acoustic pressure. The transducers were tested in receive mode by generating ultrasound from a commercial 50 kHz, air-coupled, piezoelectric transducer . An in-house built 20 dB EMAT amplifier was connected to the receiver transducer. Transducers B and C were also tested in transmit–receive mode 40 cm apart, with transducer B as transmitter and C as receiver.The equations describing harmonic vibrations in thin, edge clamped, circular plates  were useThe transducers were driven with an in-house built, wideband EMAT current pulser. The current pulse through a 100 mΩ resistor in series with the coil from transducer A is shown in Generating ultrasound with an electromagnetically-excited flexural transducer is straightforward. Large pressure amplitudes were observed 40 cm from the front face of the transducers, as seen in The frequency content of the generation signals from the transducers is shown in fL,s see , whereasA static magnetic field orthogonal to the eddy current is not necessary for generating ultrasound (as seen in Q) devices, this would not be true for a broadband source. Transducers B and C do not accurately capture the centre frequency of the Airmar transducer, which is overestimated by approximately 2 kHz. This is another consequence of using a resonant system, where the transducers will skew the received signal frequency towards their mode frequency. As seen in the transmission data  behind the piezoelectric element, which dampens the oscillations without significant impact on the flexural properties of the transducer. This method should apply equally to EDFTs. Specifically for EDFTs, because of the non-contact coupling, the back of the flexing plate could be coated with an elastic material, to increase mechanical damping. Potentially, a specific quality factor could be achieved by optimizing the material and thickness of this damping layer.The self-induced field was shown to be sufficient for the generation of ultrasonic waves, which is a significant discovery, as it allows for the construction of air-coupled electromagnetically excited ultrasound generators without the need of permanent magnets. However, it was also shown that the output pressure amplitude could easily be enhanced by the inclusion of a magnet. The dominant generation force contribution for transducers A and B is from the self-induced field. The dominant generation force for transducer C is from the static field.Q factor. The output amplitude of the EDFTs could hence be increased significantly by using an appropriately narrowband pulser.The EMAT pulser used to excite the EDFTs was not optimized for flexural systems. In general, for flexural transducers, a tone-burst (multi-cycle) excitation signal is used, to make use of the resonance behaviour and high In reception, all three transducers were able to pick up a pressure wave with 9 Pa amplitude that was produced by a large-aperture commercial air-coupled 50 kHz transducer. The SNR of the receive signal was quite low, around 2 dB for single shot data, and indicative of a low sensitivity. The results are, however, still significant as a proof of concept.Since the reception mechanism is not the reciprocal of the generation mechanism for EDFTs, a good generator design does not necessarily make a good receiver, as was indeed seen when comparing the transmit and receive signals in


Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure. This paper presents a novel structured light sensor designed specifically for operation in outdoor environments. The sensor exploits a modulated sequence of structured light projected onto the target scene to counteract environmental factors and estimate a spatial distortion map in a robust manner. The correspondence between the projected pattern and the estimated distortion map is then established using a probabilistic framework based on graphical models. Finally, the depth image of the target scene is reconstructed using a number of reference frames recorded during the calibration process. We evaluate the proposed sensor on experimental data in indoor and outdoor environments and present comparative experiments with other existing methods, as well as commercial sensors. Over the last few years, we have witnessed the rapid growth of 3D imaging technologies in various application areas, ranging from autonomous navigation of robots, drones or cars ,2,3, medExisting 3D imaging techniques can be divided into two main categories: (i) active and (ii) passive. Active techniques utilize an active source of illumination to project a suitably-devised pattern of structured light onto the target scene and then perform 3D reconstruction based on temporal or spatial distortions of the projected pattern caused by interactions with the target scene. Examples of active techniques include standard structured light approaches ,18,19, tWhen applied in outdoor environments, 3D imaging techniques are expected to provide accurate depth information regardless of the external lighting and atmospheric conditions, which are known to negatively affect the existing range measurement techniques. This property is crucial for various outdoor applications that require reliable depth information to function properly. This paper addresses the problem of outdoor depth imaging with structured light approaches and presents a novel sensor designed specifically for outdoor deployment . The senWe make the following three contributions in this paper: (i) we present a novel 3D imaging sensor that supports robust acquisition of spatial distortion maps and is able to generate accurate depth maps in challenging outdoor environments; (ii) we describe the complete hardware and software  design of the sensor; and (iii) we present a comprehensive experimental evaluation, as well as comparative results with competing techniques and existing commercial sensors.The paper is organized as follows: This section presents a short overview of a novel sensor designed for depth image acquisition. The sensor presented was developed as part of our research efforts with respect to an active triangulation system (ATRIS) capable of capturing depth images in difficult settings; for example, under exposure to strong incident sunlight. The sensor relies on the established concept of depth image acquisition based on structured light, in which a light pattern is first projected onto a target scene, and the shape  of the scene is then inferred based on the spatial distortions of the projected pattern and the (known) geometrical properties of the prototype.The image acquisition procedure uses specialized hardware (comprised of a laser projector and a high-speed camera) to project a structured light pattern onto the target scene with the goal of capturing an image of the distorted pattern . The procedure is based on the recently-introduced concept of modulated pattern projection ,34, whicThe light plane-labeling procedure establishes the correspondence between all parts of the projected light pattern and the detected pattern that has been distorted due to the interaction with the target scene. The procedure uses loopy-belief-propagation inference over probabilistic graphical models (PGMs) as proposed in  to solveThe 3D reconstruction procedure reconstructs the depth image of the target scene based on (i) the reference frames of the light pattern projected onto a planar surface at different distances from the camera and (ii) the established correspondence between parts of the projected pattern and the detected distortion map.A schematic representation of the three key components of the ATRIS sensor is shown in A detailed description of all three key ATRIS components is presented below.This section describes the modulated pattern acquisition procedure used in the ATRIS sensor. The section starts by presenting the hardware setup used in the sensor and then proceeds by describing the pattern acquisition procedure and its characteristics. Note that the underlying concept of the acquisition procedure was originally introduced in .The hardware setup shown in a used inTo acquire one image  of the projected light pattern, the camera first captures a series of images of the target scene (we refer to these images as sub-frames). Every time an image (sub-frame) is taken, the laser projector is turned either on or off depending on the current value of the pseudo-random binary control/modulation sequence register . If the tern see b. Based As illustrated in Noise suppression: The modulated acquisition procedure is robust for various types of noise. If information related to the visual appearance of the target scene is treated as “background noise,” then the procedure presented obviously removes the background noise as long as the control/modulation sequence employed in the FPGA register is balanced . Because demodulation is a pixel-wise operation, the acquisition procedure presented also suppresses sensor noise  caused, for instance, by poor illumination or high temperatures, where a simple pair-wise sub-frame subtraction would not suffice.Operation under exposure to incident sunlight: Even if the illumination of the target scene by incident sunlight is relatively strong, the modulation sequence is capable of raising the level of “signal” pixels sufficiently to recover a good-quality image of the projected pattern. This characteristic is related to the noise suppression property discussed above, because incident sunlight behaves very much like background noise under the assumption that the intensity level of the sunlight is reasonably stable.Mutual interference compensation: With the modulated acquisition procedure, it is possible to compensate for the mutual interference typically encountered when two or more similar sensors operate on the same target scene. This can be done by constructing the control/modulation sequences based on cyclic orthogonal  codes, in which the cross-correlation properties of the modulation codes are exploited to compensate the mutual interference  mounted in front of the laser is used to split the laser beam and produce a structured light pattern comprising 11 parallel light planes see a. For thThe modulated acquisition procedure presented in the previous section results in a binary image (or frame) of the projected pattern that forms the basis for depth image reconstruction. As pointed out in The binary distortion map captured with the ATRIS acquisition setup contains a large number of binary regions. The 11 light planes that constitute the structured light pattern are usually not detected as large connected binary regions in the captured image, but in the form of shorter, potentially discontinuous line fragments (we refer to any connected binary region (using eight-adjacency) in the image as a line fragment), as shown in The structured light pattern used in the ATRIS sensor consists of 11 parallel light planes. Solving the correspondence problem, therefore, amounts to finding the correct light plane label for each of the connected binary regions in the captured binary distortion map . AlthougI, that the scene points illuminated by the projected pattern are encoded with a pixel value of one and that all other pixels are encoded with a value of zero. Furthermore, assume that the non-zero pixels that form the line fragments are grouped into pixel segments of fixed width . Let us now denote the set of all pixel segments in the distortion map I as i-th pixel segment  problem can then be defined as the mapping ψ that assigns each pixel segment from The illustrated labeling problem can be formally defined as follows: assume that the detected light pattern is represented in the form of the binary distortion map We follow the ideas presented in ,33 and fX from N random variables I taken at time instance t. Similarly, each edge is associated with a factor that models the functional relationship between the vertices (random variables) connected by the edge. Solving the labeling problem defined in Equation , horizontal (x axis) and “temporal” (t axis) directions. The right side of The PGM-modeling procedure used for the ATRIS sensor is illustrated in  pattern . They arZ denotes the partitioning function and the sets N can in general also take different values at different time instances. The joint distribution is defined only for the case of two consecutive frames )39]), whUnlike the toy example in For the ATRIS graph construction procedure, horizontal neighbors are defined as connected pixel segments . In the middle image of Vertical neighbors in the ATRIS sensor are defined as pairs of pixel segments that are not connected, but share at least one pixel at the same t, that share at least one non-zero pixel at the same spatial coordinates. This definition requires no tracking of the pixel segments over time and is extremely simple to implement. On the right side of Finally, temporal neighbors are defined as pixel segments belonging to detected patterns recorded at two consecutive time instances, I. However, to be able to conduct inference on the graph, factors between pairs of neighboring vertices (or on a single vertex) that model the dependencies between the random variables associated with the vertices need to be defined, as well. The procedure for defining the factors used in this paper is described in the next section.The definitions presented define the topology of the PGM  constructed from the given input image Factors represent functions of random variables. Typically, factors model the dependencies  between neighboring vertices and, hence, represent functions of two random variables. Alternatively, they relate only to a single vertex and act as functions of a single random variable. In the modeling procedure used for the ATRIS sensor, factors are used to model the relationships between horizontally-, vertically- and temporally-neighboring pixel segments, and unary factors are added to include knowledge about the structure of the projected light pattern.Horizontal factors ne label . The fitf denotes a linear function of the difference of two labels. The function f decreases monotonically with the label difference:Vertical factors h defines the slope of the linear part of the function f; The parameter Temporal factors  that is :(6)ϕt line fragments above and below the current x-position of the red segment. We then label the pixel segments found at the current x-coordinate consecutively from the bottom of the image up and increase the likelihood of the label assigned to the red-segment by some arbitrary constant q. If we are able to find M pixel segments at the given x-coordinate, only the likelihood of a single label is increased   4:for all x-coordinates of the pixel-segment corresponding to do 5:M biggest line fragments in I having a pixel segment at the current x-coordinate  ▹ find (at most) 6:k, of the pixel-segment  constituting the graph, for which the range of possible values is given by the set of light plane labels Equation ). The asEquation  and X^t MAP estimation can be conducted using different inference techniques; for example, ,39,41. IIn order to reconstruct a depth image of the target scene based on the labeled distortion map, we use a simple processing approach involving reference frames of the projected pattern taken at various distances from the camera.To capture the reference frames, we start by placing a planar surface parallel to the stem see a at someM line fragments Let us denote the distances at which the reference frames, I located at image coordinates x-coordinate, i.e., y coordinate; that is:y-coordinate of the i-th line fragment in the reference frame z-coordinate of the given pixel with respect to the camera’s coordinate system:x and y coordinates of the pixel (in camera coordinates) are computed using the hardware’s intrinsic parameters, which can be estimated with standard techniques [I and results in a sparse depth map, which is interpolated during the last processing step to fill in the missing values.Consider a non-zero pixel  from the distortion map chniques . The proThis section describes the experiments conducted to demonstrate the merits of the developed sensor and to evaluate its performance on experimental data using quantitative performance metrics. We start the section by presenting experiments related to the characteristics of the sensor and the proposed acquisition procedure, proceed by providing results on the performance of the proposed light plane labeling technique and conclude the section with some examples of 3D reconstructions of scenes generated with our ATRIS sensor.One of the main merits of our pattern acquisition procedure is the fact that it is possible to deploy several depth sensors exploiting our procedure in the same environment. In fact, we demonstrated in  that it Another important aspect of the developed pattern acquisition procedure is its robustness to ambient illumination and the presence of incident sunlight. To demonstrate this characteristic, we again provide a few  illustrative examples. We first present sample results for a simple indoor scene imaged in three distinct illumination conditions: (i) under ambient lighting with no additional illumination directed at it  as our ATRIS sensor. The results of this experiment are shown in For our third and last experiment with the acquisition procedure, we set up another outdoor scene and compared the performance of our ATRIS sensor with the first and second generation Kinect sensor. The second generation Kinect (v2) uses time of flight (ToF) technology and requires a full-fledged GPU supporting DirectX 11 to produce depth maps. Due to the required computing resources and different technology, Kinect v2 is not a direct competitor to our ATRIS sensor (which runs on a simple FPGA), but is included in our comparison to demonstrate the performance of a state-of-the-art depth sensor. The qualitative comparison is presented in The distortion maps see  acquiredh, etc.). In practice, it is necessary to fix the open-hyper parameters in such a way that the labeling technique exhibits the best possible performance. We therefore construct the first dataset from 152 images of a simple indoor scene, which is suitable for our purposes, because images taken indoors contain very little noise. The indoor scene comprises three objects positioned over a rotating table that change position backwards, forward, left and right, thus creating different depth discontinuities. The second dataset used for our experiments is a more realistic dataset of outdoor images. Here, we record 15 images of a scene containing a moving vehicle and a person passing between the vehicle and our ATRIS sensor. The images in this dataset contain objects with more complex geometry and are used to evaluate the performance of the labeling technique with fixed hyper-parameters.The first dataset serves as our development set and is used in the experiments for tuning the open-hyper parameters of the labeling procedure  the correct labeling rate (CLR):A few sample images from both datasets and color-coded examples of the ground truth are shown in As indicated above, the goal of the first series of experiments is to examine the impact of various hyper-parameters of the proposed techniques on the labeling accuracy. Towards this end, we first set the values of all hyper-parameters to a default value, then change a single parameter at a time and observe how the labeling accuracy changes with respect to the varying parameter. Even though the hyper-parameters of the proposed labeling technique are generally not mutually independent, we can, nevertheless, obtain a rough impression of the performance of the proposed method with respect to the varying parameter. We only make use of the first, the indoor dataset, in this series of experiments.h  and oveEquation ) have onEquation ), on theEquation . These rBased on the results of this series of experiments, the following parameter values are selected for the subsequent experiments on the outdoor dataset: To gain insight into the characteristics of the proposed labeling approach and examine its behavior on more challenging data, we run several tests in the second series of experiments. These tests are conducted on the outdoor dataset and aim at: (i) examining the rationale behind defining the PGMs using horizontal, vertical and prior factors, (ii) evaluating the importance of temporal information on more challenging data and (iii) comparing the proposed technique to the existing labeling techniques.To demonstrate the importance of each of the factors in the PGM, we conduct several tests. During each test, we remove a single factor and keep the rest.  is assigned the label 1; the second detected non-zero pixel at the given x-coordinate is assigned the label 2, and so on; until all 11 labels have been assigned.The naive labeling approach (NLA), which assigns light plane labels to the detected non-zero pixels in a consecutive manner. The first non-zero pixel at the given The labeling approach based on prior information (PR), which assigns light plane labels to the detected non-zero pixels by constructing a PGM based on prior factors only. This approach represents a refined version of the naive labeling technique introduced above.The reference approach from Ulusoy et al. (RUL) , which aNext, we demonstrate the performance of the proposed labeling technique in comparison with other techniques that can be used for labeling the structured light pattern used in the ATRIS sensor. The implemented reference techniques are related to other structured light approaches from the literature that exploit light patterns comprised of parallel stripes (e.g., ,44,45,46Note that even the naive labeling approach results in a relatively high labeling accuracy with a CLR of 0.888. This approach is expected to work well in simple conditions, where there is no noise in the detected distortion maps and no large depth discontinuities are present in the scene. All other techniques improve the performance of the NLA technique with the proposed approach resulting in a CLR of 0.989.All in all, the results of our experimental assessments suggest that exploiting spatio-temporal information for determining light plane labels in our ATRIS sensor is a feasible approach that results in state-of-the-art performance. The PGM approach is capable of assigning the correct label to most pixel segments of the detected light pattern even if large depth discontinuities are present in the scene observed. To visually demonstrate the efficacy of our approach, a few illustrative results of the labeling procedure are presented in Once the light plane labels have been assigned to all parts of the detected light pattern, depth images of the observed scene can be reconstructed using the procedure presented in The second example in We have presented and experimentally demonstrated the merits of a novel sensor for depth image acquisition. The sensor presented is based on the recently-introduced concept of modulated pattern projection , which eAs part of our future work, we plan to further improve the sensor presented. One of the main drawbacks of the current implementation is the structure of the projected light pattern, which affects the resolution and quality of the acquired depth image. To address this issue, we intend to explore structured light patterns that can be used with the PGM-based labeling procedure presented. The goal here is to devise a pattern that ensures an even better quality of the captured depth images compared to what is possible with the current sensor. A possible way to achieve this is by using more lines in the light pattern or combining the existing pattern with line scanning techniques capable of generating dense depth maps.The current implementation of the ATRIS sensor is suitable for outdoor applications, such as collision avoidance or autonomous navigation, where approximate depth maps need to be acquired as reliably as possible and the resolution of the depth maps is not of major concern. Another application domain for our ATRIS sensor is computer vision applications exploiting action recognition ,48, pose


Transition zones in railway tracks are locations with considerable changes in the rail-supporting structure. Typically, they are located near engineering structures, such as bridges, culverts and tunnels. In such locations, severe differential settlements often occur due to the different material properties and structure behavior. Without timely maintenance, the differential settlement may lead to the damage of track components and loss of passenger’s comfort. To ensure the safety of railway operations and reduce the maintenance costs, it is necessary to consecutively monitor the structural health condition of the transition zones in an economical manner and detect the changes at an early stage. However, using the current in situ monitoring of transition zones is hard to achieve this goal, because most in situ techniques  are labor-consuming and usually not frequently performed (approximately twice a year in the Netherlands). To tackle the limitations of the in situ techniques, a Satellite Synthetic Aperture Radar (InSAR) system is presented in this paper, which provides a potential solution for a consecutive structural health monitoring of transition zones with bi-/tri-weekly data update and mm-level precision. To demonstrate the feasibility of the InSAR system for monitoring transition zones, a transition zone is tested. The results show that the differential settlement in the transition zone and the settlement rate can be observed and detected by the InSAR measurements. Moreover, the InSAR results are cross-validated against measurements obtained using a measuring coach and a Digital Image Correlation (DIC) device. The results of the three measuring techniques show a good correlation, which proves the applicability of InSAR for the structural health monitoring of transition zones in railway track. Transition zones in railway track network are locations with considerable changes in the vertical support structures, which are located near engineering structures, such as bridges, culverts and tunnels . A typicTo ensure the safety of railway operations and the reduction of maintenance costs, it is necessary to inspect the structural health condition of transition zones at a high frequency. Structural Health Monitoring (SHM) system would allow the detection of potentially dangerous situations at an early stage to provide an efficient way to extend the operational life of railway structures, while not interfering with train operations . The widTherefore, this paper proposes a contactless SHM system that uses satellite SAR (Synthetic Aperture Radar) measurements to complement the current in situ measurements. The satellite SAR measurements are obtained on a bi-/tri-weekly basis, and with high precision (at millimeter level) ,26. By pTo demonstrate the InSAR feasibility and its potential for the structural health monitoring in transition zones, a transition zone is measured and analyzed using the InSAR techniques. Further, the InSAR results are cross-validated using the measurements from a measuring coach and a DIC-device. The paper is organized as follows. The settlement mechanism of the tracks in transition zones is described in Since the vertical track stiffness determines the rail deflection due to passing trains, the large abrupt change in the vertical track stiffness in transition zones results in a rapid change in the elevation of wheels . In otheAs a result, the wheel-rail interaction forces in transition zones are significantly amplified ,42,43,44As shown by the “dip” in The affected zones located next to engineering structures often have various lengths, e.g., 4 m in ,24, 7 m The current structural health-monitoring measurements for transition zones are mainly performed by measuring coaches, leveling instruments, and linear variable differential transformers (LVDT). Leveling instruments are the optical instruments used to establish or verify the points in the same horizontal plane, which were used in ,22,47. LIn most countries, track geometry is mainly measured by the inertial measuring system of measuring coaches. For example, in Japan, the condition monitoring has been accomplished using measuring coaches since 1964. The measurement results are compared at a common point of reference. Precise allocation of a test result to a rail is possible via accurate DGPS  location reference. For example, the EURAILSCOUT UFM120 measuring coach has been used since 1999 in the Netherlands, as shown in x, y, z. The position of measuring coaches is determined by means of double integration of the acceleration measurements. The track position is then measured in relation to the resulting coach position by using relative displacement transducers . Assuming that the track settlement on engineering structures is negligible, the settlement of ballast tracks can be calculated by using the track on engineering structure as a reference.The inertial measuring system consists of three high-precision gyro systems and accelerometers for all three spatial directions This geometry measuring system is on the front side of car bodies, positioned between axles. Track geometry is measured at every 0.25 m, stored in onboard computer systems processed and printed immediately during the ride. All the measured data can also be plotted as a graphical representation, as well as be exported to the external data medium .The advantage of measuring coaches is that it can measure multiple parameters at the same time, including track geometry, track videos/photos, and acceleration of bogies/car bodies. However, the cost of performing the measuring coach measurement is high so that its frequency is relatively low, for instance, twice per year in the Netherlands.DIC is an optical method using tracking and image registration techniques for accurate measurements in images. A reference picture is captured before loading, and a series of pictures are taken subsequently during the loading process. The images are analyzed using a numerical matching technique to identify the most similar patterns in the subsequent images, which is based on the assumptions that the pattern is approximately constant between successive images and the local textural information is unique. The matching algorithm compares the image subsets in the reference image with the image subsets in the current image . MatchinThe DIC devices have been used to monitor the displacement of rails , displacDIC devices can measure detailed dynamic responses of the tracks at specific locations in transition zones. However, the cost of performing DIC measurements is also at a high level since it needs the manual operation. Currently, the condition inspection of transition zones using DIC devices is only performed occasionally and locally for the purpose of research.InSAR techniques have been widely applied in monitoring “natural” geophysical processes, and recently, they have been successfully deployed for structural health monitoring of infrastructure, such as railway, buildings and bridges ,31,63,64InSAR uses two or more satellite radar measurements  of the same area to extract ground deformation remotely and periodically, with millimeter-level precision. A SAR satellite operates using a side-looking geometry and illuminates a swath parallel to the satellite’s nadir track by transmitting a series of radar pulses from a fixed antenna , see FigWhen two (or more) SAR images, acquired at the same time from a different orbital track or acquired at the separated time but imaging the same area, are available, the temporal evolution of ground targets can be retrieved by the InSAR techniques. For instance, the two SAR images are called the master image and slave image; see lave see b, as inThe topographic phase caused by the surface geometry, The kinematic phase due to surface or object displacementThe phase contribution from the atmospheric signal delayOther random additive noise.The absolute interferometric phase It is of importance to properly unwrap the interferometric phase and mitigate the atmospheric and noise influence, in order to generate a high-precision deformation component. Therefore, the persistent scattered interferometric (PSI) method ,66, usinComparing to the measuring coaches and DIC devices, which will be introduced in To study the capability of InSAR application on the structural health inspection of transition zones, a transition zone is analyzed. The settlement results for the transition zone derived from SAR data processing are compared with the results of the measuring coach and the DIC device performed on the same transition zone (as introduced in The track settlement in the transition zone was measured by the SAR satellites. 153 TerraSAR-X satellite images (30 × 50 km) were acquired between April 2009 and October 2015, in descending orbit. The wavelength of the on-board radar sensor is 3.1 cm. The PSI method is used to process the SAR data. The measuring coach is EURAILSCOUT UMF 120 as described in In this section, the InSAR measurement results are first discussed. Then the cross-validation is performed using the two in situ measurements from the measuring coach and the DIC devices.The track settlement measured by the TerraSAR-X satellites in the transition zone is shown in As shown in To study the track settlement in different regions of the transition zone, the InSAR measurements in three representative points of the transition zone are further analyzed, including one point from the ballast track close to the bridge, one point far from the bridge and one point the on the bridge  and the other is far from the bridge (240 m), as indicated by the two white arrows in It is remarkable to note that the transition on the right side in  of the bThe satellite, the measuring coach and the DIC measurement unveil the structural health condition of the transition zone. Since the measured objects from these three systems may differ, the relation between the three systems is discussed in this section.The track deformation is explained in The normal elastic deformation is determined by the track stiffness, including the stiffness of rail, fastening system, and substructure. It can therefore be different between the track on the embankment and the track on the structure . The abnormal deformation represents the void under the sleepers (hanging sleepers). They often exist in transition zones ,22,38, wAs a result, the InSAR techniques can measure the settlement of the unloaded track, which is: In principle, the abnormal elastic deformation  and to provide guidance (suspect locations) for the local inspection or maintenance.The questions needing further research are (1) how to ensure PSs in the measured transition zones and (2) how to guarantee the measurement accuracy in extreme weather.


AbstractCitizen Science (CS) as a term implies a great deal of approaches and scopes involving many different fields of science. The number of the relevant projects globally has been increased significantly in the recent years. Large scale ecological questions can be answered only through extended observation networks and CS projects can support this effort. Although the need of such projects is apparent, an important part of scientific community cast doubt on the reliability of CS data sets.The pilot CS project COMBER has been created in order to provide evidence to answer the aforementioned question in the coastal marine biodiversity monitoring. The results of the current analysis show that a carefully designed CS project with clear hypotheses, wide participation and data sets validation, can be a valuable tool for the large scale and long term changes in marine biodiversity pattern change and therefore for relevant management and conservation issues. A sharply increasing trend in data being gathered by citizen scientists (CSs) without any scientific background has been recorded in the last couple of decades in many different scientific issues. This trend has tremendously facilitated by the platforms developed on the internet, the applications for mobile phones and finally by the popularity and the scope of the SCs activity . Three fHundreds of thousands of CSs daily participate to projects related to climate change, invasive species, conservation biology, ecological restoration, water quality monitoring, population ecology and biodiversity monitoring . TraditiThe CS projects can be divided into two broad categories: (a) those that they have a scientific approach for the data collection at their hardcore, that is at all those steps from a purely scientific question to the analysis of the data and interpretation of the results and, (b) those that primarily work to address education, outreach and awareness purposes .Despite the willingness of the individual citizen scientists to collect many data, a crucial point for the success of any CS project is the quality of the datasets they create. One of the main challenges of CS projects from scientific and policy perspective is the quality of the data in terms of accuracy and precision, spatial and temporal resolution, robustness, documetation, and access . Many stMore than 200 CS projects are nowadays active  and, althttp://www.comber.hcmr.gr) has been established in the region of the eastern Mediterranean Sea in the context of the ViBRANT  project. It has been designed for divers and snorkelers who are interested in participating to coastal marine biodiversity projects. The project aimed at engaging CSs in a marine coastal biodiversity observation network. The main scientific objective of the project has been to test the willingness of SCUBA divers (fun divers or diving club members) to join the project and the quality of the collected data and relevant information.A pilot CS project named COMBER  in the emerging multivariate patterns; in other words, emerging multivariate patterns are independent from the CS data collector and his/her experience; (b) On the issue of the representativeness, the H0 scientific hypothesis, in order for the data collected to be valuable for biodiversity assessment studies, is that the fish species lists of the samples collected by each diver, along with their higher classification (as an approximation of the phylogenetic/taxonomic diversity of a sample), should be random assembly of the broader fish inventories from regional pools. In this way randomness is used a a means to infer representativeness of the collected datasets.Therefore, the purpose of this study is to test the robustness and the representativeness of CSs data sets collected in the course of the COMBER project in order to address the marine coastal biodiversity monitoring task based on a single taxon: The most common coastal fish species of the eastern Mediterranean, which is used as a proxy in order to test two hypotheses: (a) The Hhttp://www.bio-watch.com) is used. This card presents fourty of the most common fish species in Mediterranean shallow habitats on a pictorial key based on morphological features, colour pattern and habitat  marine habitats, with the potential to expand the concept into the whole Mediterranean basin or any other region . The fis habitat . The COMThe participants had the option to keep notes for their observations in two ways: presence/absence and relative abundance of four different orders . A short seminar has been provided before each dive excursion, with the view to introduce the SCUBA divers into the concept of the specific CS project. A seminar, of a fifteen minutes presentation, explaining the way of identifying the fish species on the card and how to record them has been delivered before the dive. At the beginning of the dive and for the first ten minutes, participants assisted by the scientists are identifying the different species they can observe and which are depicted on the fish card without recording them. After this ten minutes period, the divers identify and record by themselves. A small de-briefing is following after each dive and all the participants are kindly requested to fill in a questionnaire regarding their past and present experience. Finally, the participants are guided to navigate in the website, create an account, fill in their diving profile and enter their species observations into the database  identification experience, (ii) diving experience and (iii) implementation year. In the first category, the knowledge background was divided into three major categories: (a) Amateur, for those divers without any previous experience in species identification, (b) Skilled, for those with some previous experience in species identification and (c)Professional, for those with a great experience in species identification. In the second group (diving experience) and independently of their species identification capacity, the participants were divided according to their diving experience into: (a) Novice (up to 20 dives), (b) Intermediate (between 21-50 dives) and (c) Experienced (more than 51 dives). In the third category (implementation year) the dataset was divided into the years of project implementation (2011-2013).In order to convert the four orders of abundance into numerical values, a new rank with four orders of magnitude was created: absent: 0, one bar: 10 individuals of a certain species, two bars: 100 and three bars: 1000. Additionally, in order to down-weight the differences in divers effort, the abundance values in the recordings of each diver (unique ID) were averaged and then the triangular similarity matrices were produced by applying the Bray-Curtis coefficient using the divers data . To test the first hypothesis, the multivariate patterns of fish card species distribution were derived by using the algorithm of the non-metric multidimensional scaling (nMDS) on the corresponding similarity matrices as proposed by The ANOSIM test calculates a sample statistic R with values ranging between -1 and 1 , where R = 1 represents an undeniable difference between the groups under comparison. The application of the routine in the PRIMER package provides a simulated distribution of possible R values on a frequency histogram and superimposes the observed value on that histogram. Observed R values outside the expected distribution are taken as statistical evidence to reject the null hypothesis (no differences between groups).www.fishbase.org). The test was run for the two different categories of data set based on: a) diving experience and b) identification experience. At each scale of comparison it was tested whether the biodiversity observation subsets, which means the species lists recorded by the divers and their higher phylogenetic interrelations, represent a random sample of the higher observational scales. The above test was performed by calculating the taxonomic distinctness indices . These indices take into account not only species ID but also their phylogenetic / taxonomic interrelations , are randomly assembled from the fish species pool of the broader area, a hierarchical approach was applied. Six different scales of observation are defined after an extended literature research: (a) Mediterranean fish inventory (b) Eastern Mediterranean fish inventory, (c) Aegean fish inventory, (d) down to 50m depth Mediterranean fish inventory, (e) down to 50m Eastern Mediterranean and (f) down to 50m Aegean. The last three of the aforementioned scales were specifically chosen because of the use of the "bio-watch" fish card created for divers and snorkelers and thus all the included species live in shallow waters and therefore are observable. For the construction of observational species list the data base from FishBase was used  while the less abundant was Rajaclavata (1/365).In total 141 divers and snorkelers (unique ID) have participated in the COMBER pilot project. The participants have submitted 365 data sets  within the three years of the pilot project implementation. More than half of the participants (61%) contribute to the database at least twice, with the highest record 28 entries from the same participant. The most common species found to be the The MDS plots Fig.  a,b illu+, Λ+) at all scales of observation are shown in  and between the years of the project initial implementation. The analysis of similarities shows that there are no statistical differences between the produced data sets in reference with the collection years, diving experience and identification skills. At this point must be underline that the stress values on both analysis (diving and identification experience) were greater than the crucial value 0.2. Based on + funnels show that at all levels of observation and for all different categories of divers the majority of the datasets fall outside the expected distribution; (b) This pattern is altered in case of Λ+ index funnels, where the majority of the participant’s datasets fall inside the funnels limits. Taking into account the theoretical background and the mathematical formula of the two indices it’s easy to explain the converse patterns obtained from both of them. There is an undeniable bias in the fish card species list and their higher classification since at least two fish families (Sparidae and Labridae) are over-represented. These two families are, indeed, very common in the shallow coastal Mediterranean waters  The Δn waters  and theyQuestions on large scale long term biodiversity patterns and their changes can be answered through CS projects. The collection of such data by exclusively scientists requires a vast amount of budget and effort. In addition, the large number of publications  provideCitizen’s participation in marine CS projects is limited comparing with the terrestrial one. The main reason for this “unbalanced representation” is the accessibility and the associated costs, which are more direct and lower, repectively, in the case of terrestrial activities. Volunteers who dropping out or becoming disinterested could possibly be convinced to come back with some degree of positive reinforcement  , by matcAt this point, it must be underlined that the citizen scientists have to be categorized in different groups, based on their contribution in the projects: a) basic level, where participants contribute only in data gathering, b) advanced level, where participants, may make suggestions and try to improve the purpose of the project and, c) professional level, where participants are involved in the setup of the project . The resTo conclude, CS projects become an increasing need for biodiversity monitoring by collecting large scale, long term data. Until now, the results of existing projects have shown that the collected data have to some extent an important scientific value if they are analyzed in the proper way. Most of the already running projects show that volunteers are able to detect important changes in communities through their data and so they have a valuable role to play in assessing change on biodiversity and ecosystems . Accordi


The Technion autonomous underwater vehicle (TAUV) is an ongoing project aiming to develop and produce a small AUV to carry on research missions, including payload dropping, and to demonstrate acoustic communication. Its navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL), magnetometer, and pressure sensor (PS). In many INSs, such as the one used in TAUV, only the velocity vector (provided by the DVL) can be used for aiding the INS, i.e., enabling only a loosely coupled integration approach. In cases of partial DVL measurements, such as failure to maintain bottom lock, the DVL cannot estimate the vehicle velocity. Thus, in partial DVL situations no velocity data can be integrated into the TAUV INS, and as a result its navigation solution will drift in time. To circumvent that problem, we propose a DVL-based vehicle velocity solution using the measured partial raw data of the DVL and additional information, thereby deriving an extended loosely coupled (ELC) approach. The implementation of the ELC approach requires only software modification. In addition, we present the TAUV six degrees of freedom (6DOF) simulation that includes all functional subsystems. Using this simulation, the proposed approach is evaluated and the benefit of using it is shown. Underwater operations attract great attention for environmental matters and potential resources, as well as scientific interest. Therefore, the need for underwater robotic systems has become more apparent . With thA team at the Technion–Israel Institute of Technology is currently developing an AUV named the Technion autonomous underwater vehicle (TAUV). The TAUV project goal is to develop and to produce a small autonomous underwater vehicle, which will serve as a technology demonstrator and a platform for various research programs. The TAUV diameter is 300 mm and its length is 3000 mm. The TAUV has four rudders installed in an “x” configuration, and its maximum operation depth is 200 m. The TAUV navigation system based on an INS aided by a DVL, magnetometer, pressure sensor (PS), and GPS when available . When suThere are two main approaches for sensor fusion between INS and other sensors: (1) loosely coupled (LC); and (2) tightly coupled (TC) . We shalThe selected TAUV INS can only receive from the DVL an external velocity vector measurement . TherefoIn this paper, we propose the extended loosely coupled (ELC) approach for calculating vehicle velocity when only partial DVL measurements are available. This is made possible by using the partial measured raw data from the DVL combined with additional information. This calculated vehicle velocity is fed back to the TAUV INS and used for aiding the INS, as in the regular LC approach.Related approaches in similar situations where partial measurements are available can be found in the literature for INS/GPS fusion or LBL positioning. In , a singlIn order to examine and demonstrate the improvement of the navigation performance under the ELC approach, we developed the TAUV six degrees of freedom (6DOF) simulation, which includes all functional sub-systems. This simulation consists of the AUV guidance, navigation and control subsystems and uses a complete hydrodynamic model. Simulation results show great improvement in the navigation performance using the proposed approach compared to the standalone TAUV INS solution.The rest of the paper is organized as follows: A top-level block diagram of the TAUV 6DOF simulation with its main subsystems is presented in Hydrodynamic and Motion block: This model calculates the forces and moments applied on the TAUV for the 6DOF rigid body motion model. The motion model solves the vehicle kinematics to produce the true values of the vehicle’s position, velocity, attitude, and acceleration. The model contains a complete hydrodynamic model derived for the TAUV.Control system block: The control system is used to stabilize the vehicle attitude by determining the angles of the four TAUV servos. The vehicle velocity is stabilized by controlling the thruster rotation speed. The TAUV control system contains four proportional integrative derivative (PID) controllers: three for the attitude control and one for the velocity control.Guidance system block: The guidance laws in the TAUV are based on target tracking methods [ methods . This suSensors generator block: The block calculates sensors’ outputs, including their errors, by statistical means. The sensors are: accelerometers, gyros, DVL, PS, magnetometer, and GPS.Navigation block: The navigation block represents the navigation system of the TAUV, including the navigation equations and navigation filter. The TAUV navigation system is presented in detail in the following section.In The navigation equations and the EKF are expressed in a coordinate frame, known as the platform frame. The platform frame is located at the center of buoyancy of the vehicle. The navigation frame is determined by the INS frame. For simplicity of the dynamics equations, we assume that the platform frame and the INS frame are located at the same point at the AUV and with identical orientation. The TAUV platform frame and the tangent frame are represented in An error state EKF is implemented in the TAUV navigation system. The error state vector essed by :(1)δxk=nce with :(2)δx=[ic state :(4)FINSample in . The matThe process noises vector is defined by:In the linear case, the measurement model is:F is given by:In the presented navigation system model, we augment the INS state vector from Equation (2) with five additional error states of the DVL: four to model the DVL biases of each beam, and one for common DVL scale factor error. Thus, the augmented error state vector:G is expressed via:The corresponding noise process matrix The DVL measures the Doppler frequency shift for each depth cell and each beam and then computes the component of relative flow velocity in the direction of each acoustic beam. Based on , the relThe DVL assembly in the TAUV is constructed in an “x” configuration relative to the platform frame (“Janus Doppler configuration”), as shown in The expression for yaw angle of each beam direction in the TAUV can be written as:A be a matrix contacting all beam direction vectors:The DVL beam velocity is modeled as:Thus, by using Equation (15), the DVL-based velocity measurement of the vehicle can be expressed by:Note that the velocity solution in Equation (17) is based on four beams, of which one beam is for redundancy due to the beam configuration. Therefore, in practice only three beams are enough to calculate the vehicle velocity.In the LC fusion technique, the DVL output to the navigation filter is the platform velocity (Equation 17)). The corresponding measurement matrix is:). The coIn the case of small-level arm DVL spec  or calcuIn the TC fusion technique, the DVL raw data is used to aid the INS. That is, each transducer measurement is fused with its INS counterpart ) and the measurement matrix [) and theThe measurement noise covariance matrix is taken from Equation (21) for each beam.As mentioned in In this section, we present the ELC approach, with four different methods for utilizing the partial measurements from the DVL  together with external information to construct the DVL based velocity estimate. This velocity is used in the TAUV navigation filter, as illustrated in This method utilizes the raw data from the DVL and the filter prediction of the velocity error in order to solve for the vehicle velocity. Without the loss of generality, we assume that beams #3 and #4 are not available. Let the matrix From Equation (23), the value of the third beam may be found as:It is important to note that now, theoretically, a correlation between the measurement noise and process noise exists, since the DVL velocity measurement Equation (25) depends on the DVL partial raw data (with measurement noise) but also on the INS velocity solution Equation (24) (with process noise). Therefore:Thus, the covariance term Equation (26), which is usually nullified in the EKF formulation, needs to be included. However, in our current analysis we neglect this term.In order to calculate the corresponding measurement covariance matrix, we need to derive an expression for the constructed third beam measurement standard deviation (STD) Using Equation (24) we derive the expression for From Equation (29) the velocity measurement noise covariance matrix is expressed via:In summary, in the proposed approach we calculate one velocity beam of the DVL via Equation (24). Combining the two beam measurement from the DVL, we solve the velocity vector of the vehicle using Equation (25).v = 0, only the other two velocity components  are to be calculated using the partial DVL data. Similar to the previous method outlined in In this method we set the sway velocity to be zero, that is Let:u and w). Thus, the relation between DVL beam velocities This matrix relates the beam velocities #1 and #2 (since beams #3 and #4 are not available) to the vehicle velocity (From Equation (32) the surge and heave velocities are:Note that the matrix u and w). Therefore, the velocity measurement noise covariance matrix that is plugged into the navigation filter is:v = 0 and has a value close to zero. Equation (36) is used for practical considerations since the TAUV INS requires a complete velocity vector with its corresponding measurement covariance.From Equation (35) we extract the velocity variances of the calculated vehicle velocity components .u or v, depending on the active transducer order. Therefore, in this approach only one velocity component measurement is fused into the filter. As in the previous approaches, we assume that beams #3 and #4 are not available. When the DVL setup is in an “x” configuration, as in the TAUV, we can derive the following relations between the components of the DVL beam direction vectors based on Equation (13):This method utilizes the DVL setup configuration in order to calculate one component of the AUV velocity, u and w or v and w—can be calculated. If the active beams are not pointing in the same direction  we cannot utilize any data from the DVL using this method. Here, (under the assumption that only beams #1 and #2 are available), we intend to solve the surge velocity u, utilizing the partial measurements from the DVL. Using Equation (37) we can express the velocity of beams #1 and #2 as:If the DVL setup is in a “+” configuration and the available beams are pointing in the same direction, the two components of the velocity vector—Subtraction gives:The sway velocity is found using Equation (39):For practical considerations in the TAUV INS, the velocity measurement noise covariance matrix must have a structure of To summarize, in the proposed approach we calculate the surge velocity component using the two beam measurements Equation (40). The filter ignores the two other velocity components (sway and heave) by setting their measurement variance to infinity. That is, in practice only one velocity component is used to aid the INS.u from the DVL raw data, while the DVL allows us access to two measured velocities. In order to utilize the two DVL measurements, we use the velocity prediction from the filter. Therefore, in this approach two vehicle velocity components are used to aid the INS: u is taken from v is calculated here. To that end, we sum the two equations from Equation (38) to derive the following expression:This method is an elaboration of the previous method derived in Thus, we have one equation, Equation (44), with two unknown velocity components, The corresponding velocity variance component is:From Equations (41) and (46) the velocity measurement noise covariance matrix that is plugged into the navigation filter is given in Equation (47). As before, for practical considerations in the TAUV INS, the velocity measurement noise covariance matrix must have a structure of In summary, in the proposed approach we use the surge velocity component calculated as in The DVL enables access to its raw data and its calculated velocity. In the case of partial measurements, the DVL cannot calculate the vehicle velocity, and only the partial raw data is available. From the raw data, we can calculate the vehicle velocity and the variance from all four approaches as presented in the previous sections. Instead of choosing which approach to use in the TAUV, in Equation (48) we employ a simple selector to pick the best vehicle velocity components out of the four approaches and use each derived corresponding velocity to aid the INS.The velocity measurement noise covariance matrix (with the corresponding velocity measurement) that goes to the TAUV INS is:In this section, we evaluate the ELC approach under three commonly used trajectories of the TAUV and other AUVs. The objective of using several trajectories is to evaluate the navigation performance of each method under different dynamic conditions. The simulation and navigation parameters used to produce the results in the paper are summarized in The navigation performance obtained using Monte Carlo runs with the 6DOF simulation is presented in terms of the root mean square (RMS) errors of the velocity vector and the attitude error under the scenarios of trajectories #1, #2 and #3. The accelerometer and gyro biases are not presented since they behave as expected when fusing any other velocity measurements. That is, when the vehicle travels with constant velocity (trajectory #1) only the z-axis accelerometer bias and x and y gyro bias are observable. When maneuvering (trajectories #2 and #3) the estimation performance improves in terms of more observable error states.We compare the performance of the ELC to the performance of the standalone INS  and TC fusion, in order to test the effectiveness of the approach. We note that the analysis was made only for DVL/INS fusion. We expect that when using other available sensors on the TAUV (such as PS or magnetometer), the performance of the proposed approach will improve but this is not the subject of the paper.In the next figures, the following notations are used: TC  are presented in All the proposed approaches improved the standalone INS performance; in particular, the NSV reduced the RMS velocity error to 0.05 m/s. In addition, the NSV approach and the VHV approach also performed better than the TC approach. The reason for this result is the assumption that the AUV does not have velocity in the sway direction, and in the case of a straight trajectory this assumption is reasonable. The PLCF obtained the worst performance but still improved the standalone INS solution by 131%.The velocity RMS errors for all the methods (including TC) are presented in In the presented study the ELC approach for INS/DVL fusion with partial DVL measurements was investigated, applying four different methods. The TAUV simulation was developed in order to evaluate the ELC methods.Results show that the presented approaches significantly improved the navigation performance of the TAUV in cases where only partial measurements from the DVL are available. In order to implement the suggested approach in the TAUV, only software modifications are needed.


This paper proposes a novel and an affordable lower limb prosthesis to enable normal gait kinematics for trans-femoral amputees. The paper details the design of a passive prosthesis with magneto-rheological (MR) damping system and electronic control. A new control approach based on plantar insole feedback was employed here. Strategically placed sensors on the plantar insole provide required information about gait cycle to a finite state controller for suitable action. A proportional integral (PI) based current controller controls the required current for necessary damping during gait. The prosthesis was designed and developed locally in India keeping in view the cost, functionality, socio-economic, and aesthetic requirements. The prototype was experimentally tested on a trans-femoral amputee and the results are presented in this work. The implementation of the proposed design and control scheme in the prototype successfully realizes the notion that normal gait kinematics can be achieved at a low cost comparable to passive prostheses. The incurring cost and power expenditure of the proposed prosthesis are evaluated against passive and active prostheses, respectively. The commercial implications for the prosthesis were explored on the basis of recommendations of ISPO Consensus Conference on Appropriate Prosthetic Technology in Developing Countries. The key objective of this work is to enable lucid design for development of an affordable prosthesis in a low-resource setting. About 30 million amputees are residing in low income countries ,2. AmongGait parameters of trans-femoral amputees including step length, inter-leg symmetry, hip exertion, and involvement of upper body are highly affected . Over thLe Torneau knee and Stanford-Jaipur knee joints have a similar polycentric design of four-bar linkage with little to offer other than stance phase stability. On the other hand, Remotion Knee is the advanced version of Stanford-Jaipur knee with curvature mimicking to address late stance phase initiation ,10. StudAlso, Andrysek et al. and Arelekatti et al. addressed the problem of abnormal gait in low-cost passive prostheses. Andrysek et al. SASPL engages or disengages depending on the loading of the prosthetic limb during weight bearing but is not able to provide early stance flexion-extension and optimal swing phase damping . ArelekaIn this paper, a novel low cost lower limb prosthesis was developed and its clinical testing and analysis are presented. A new foot plantar insole feedback-based control strategy was developed and implemented to provide necessary damping using an MR damper. The prosthesis enables the amputee to realize the able-bodied normal gait kinematics and meets the functional, socio-economic, and aesthetic needs of a trans-femoral amputee and is affordable even in low-income economies. The paper also focuses on essential design features and requirements of a prosthesis to assess its commercial viability in these countries such as low cost, light-weight, functionality, biomechanically appropriate, durable, using locally available materials etc., based on the recommendations of ISPO Consensus Conference on Appropriate Prosthetic Technology in Developing Countries and elsewhere ,20,21,22This rationale of the prosthesis design is presented in this section. The trans-femoral prosthesis shall serve the major function of providing stability during stance phase and controlled flex-extension during the swing phase of a gait cycle. In the stance phase, the stability was ensured by providing suitable resistance to knee joint flexion while the user transfers body weight from the sound/healthy limb to the prosthetic limb. Similarly, the swing phase flex-extension was controlled by regulating walking speed . Both thA study conducted by Herr and Wilkenfeld  serves aIt is proposed that a single damper with a controllable damping capacity, such as an MR damper, if used with suitably designed sensing modality, driving circuitry, and control architecture, can provide able-bodied gait kinematics yet at a cost below par with advanced passive prostheses. Here, strategically placed sensors on the plantar insole provide sufficient information for normal gait kinematics, thereby enabling the control of the current in the MR damper during the different phases of a gait cycle. This forms the basis for the design of a simple MR damper-based knee joint mechanism and its control with plantar insole feedback.The design of the prosthesis’ mechanical structure is shown in ®. During the simulation along the entire knee range, there was no interference between the joint links, bars, plates, and braces. With proper machining of all the custom components, the mechanism was assembled by bolting the braces to the knee joint. The MR damper was shoulder-bolted with the links of the assembly. Bearings were employed for smooth motion of joint between knee body plates and link rods. Off-the-shelf components such as connectors, adaptors, and the Sach foot were employed to extend the design with existing prosthetic components.The piston rod and outer cylinder of the MR damper were fixed to the knee joint and the shank individually with swing link and lower-link rods. The upper-link and lower-link rods are attached to the braces. The upper link rod and swing-link rod form the knee axis and the control axis of the prosthesis, respectively; the upper link rod along with knee body articulates the knee braces via the knee axis. The control axis primarily responds to the ground reaction forces that cause relative motion between the knee and the braces. The swing-link rod transfers the forces generated by the MR damper during locomotion. The biomechanical mechanism of the control axis has been extensively reported elsewhere . The conPlantar insoles are extensively used for gait analysis of many locomotion impairments ,29. Alth®. The insole was printed using the silk screen printing process . In this process, silver-conducting ink and dielectric was used to print the circuit. While designing the insole, it was important to determine the minimum thickness of the dielectric layer and the amount of pressure that the insole may undergo during walking. It enables the designer to use an optimum thickness of the dielectric layer that is sufficient to trigger the events. For the initial prototype, this trade-off between minimum dielectric layer thickness and maximum loading was made using the ground reaction force (GRF) profile of the amputee on a force plate.The circuitry and mechanical dimensions of the insole were designed using Coral DrawTo feed the desired current to the MR damper at the given time instant, an electronic circuit that uses sensor readings was designed to regulate the current. The MR damper requires variable current to provide variable damping; the MR damper current can be varied by varying the input DC voltage across the load. It was achieved by a step-down DC/DC converter, controlled by a pulse width modulation (PWM) scheme. The duty cycle of the PWM signal was varied to reduce the error in the reference and actual feedback current. Error in reference and feedback current can be reduced to a permissible extent in desirable time by employing a proportional integral (PI) controller. The actual feedback current was measured with a Hall current sensor ACS712. The power circuit includes a battery bank rated at 9 V and 2.4 Ah, a power MOSFET, freewheeling diode, low pass LC filter, and MR damper. The power and control circuits were isolated with an optocoupler and gate-driving circuit to ensure smooth operation. The control circuit includes the sensing mechanism along with a control unit as shown in A two-level control scheme was employed to achieve able-bodied gait kinematics as depicted in This ordered behavior of state machines are utilized in neuro-prostheses with event triggered control ,35. The In state 0, the knee flexes near to the maximum stance flexion. A relatively high damping was applied during this state to prevent buckling at the knee due to the user’s weight. During state 1, the knee begins to extend after maximum flexion. The rate of extension was lower than that of flexion during state 0, thereby requiring a high damping. The level of damping shall account for slow extension in order to avoid potential slamming of rotating parts to the knee housing and stoppers. State 2 involves the flexion-extension while the heel is off the ground and the sound limb is sharing the body weight, a moderate damping shall serve the purpose here. State 3 encounters the extension with a high rate of change and low body weight bearing; a low damping will provide the desired profile. The final state (state 4) represents a large knee flexion and associated extension after flexing to its maximum. The rate of change of flexion-extension suggests a very small damping for the same. All states can be tuned to suitable current references which can be accurately followed by a well-designed PI controller. The response of the PI controller must be fast and accurate as it lies in the inner loop of the control architecture.The developed prosthesis was tested on a 24-year-old male  unilateral trans-femoral amputee in India as part of the target population. The subject was using a commercially available passive prosthesis (single axis extension assisting prosthetic knee joint) for the last three years post traumatic amputation. The amputee mobility predictor (AMP) score of the amputee was more than 35, i.e., above the K3 activity level. The dynamic alignment of the prosthesis was performed by the trained prosthetist who is one of the co-authors of the current work. The participant did not have any musculoskeletal injuries, sensory or neurological impairment or related disabilities other than trans-femoral amputation. The study was approved by the All India Institute of Medical Sciences ethics committee (Ref. No. IEC-35/09.02.17). The participant was explained the experiment protocols before starting the experiment and written consent was obtained.The controller parameters were tuned sequentially considering one controller at a time. Firstly, the innermost current controller was designed for the electrical time constant of the MR damper, so that a maximum current of 1A could be attained with a desired speed and accuracy. The gain parameters were tuned separately, and the speed was optimized after attaining a fair accuracy. It was achieved by the Ziegler–Nicholas method of tuning the PI controller. After tuning of the PI controller, i.e., the current controller, the prosthesis was fitted on the subject for familiarization. When the subject was able to walk properly with certain level of comfort, the prosthesis was then considered ready to be tuned for the secondary level of control. As stated in After obtaining an initial set of parameters, all the values were iteratively fine-tuned to attain a gait characteristic closer to that of a healthy individual. The knee angle was used as an essential quantitative measure to observe the performance. Subject feedback along with that of the trained prosthetist was considered primary qualitative criteria to tune the parameters. This type of tuning avoids unnecessary discomfort to the subject. The final set of current references are depicted in Once the prosthesis was tuned for level walking, the subject was given sufficient training time to get proper acclimatization to it. A number of training sessions were conducted for the subject in accordance with the training methods reported in several studies ,39,40,41The subject was asked to walk on a 24.38 m (80 feet)-long corridor at a uniform subject comfortable speed. The initial walk of 3.05 m (10 feet) and 3.05 m of walk towards the end were excluded to avoid any deviation in biomechanical data that may arise due to acceleration and deceleration during beginning and ending, respectively. Hence the walking variables were measured over an active length of corridor of 18.29 m (60 feet) which was assumed to be uniform. For the subject  was used to record the data with a sampling frequency of 100 Hz. The data was filtered in MATLABKnee joint angle is the most established parameter to evaluate the kinematic performance of a prosthesis . The meaExternal power requirement is the major concern for both active or semi-active prostheses. To evaluate the performance of the prosthesis, the assessment of power consumption was carried out by measuring the average current through the battery. In this study, the average electrical power provided by battery bank and the consumption by MR dampers were measured by Hall Effect current sensors (ACS712). The input electrical power consumed in a gait cycle can be evaluated from The response of developed prosthesis is seen in The lack of smooth stance-to-swing transition is a major problem witnessed in many of the previous prostheses ,37. ThisThe control of a semi-active or active prosthesis is usually achieved by highly sophisticated methods including complex sensing, data driven advanced AI, and state of the art embedded systems ,26. All The study establishes that a foot plantar insole possesses significant information required to control the prosthesis. The basic principle of extraction of information from a plantar insole lies in ROS created by the wheel-like mechanism of the foot during locomotion, as also reported earlier ,31. DiffThe developed prosthesis was designed in-house, fabricated, and assembled in the lab in India. Other than the MR damper, all the components were customized and locally manufactured with locally available materials. In an initial attempt to test the feasibility of the control scheme in less time, the fabrication of the MR damper was avoided and the most widely used MR damper (Lord Corporation Model RD-8041) was imported. The total cost of developed prosthesis is 17,000 INR excluding the damper; a suitably customized, locally designed, and manufactured MR damper is estimated to incur 5000 INR more, amounting the total cost to 22,000 INR. This cost is comparable to commonly available low cost passive prostheses which typically cost ranging from 6000 to 150,000 INR . The totWhile importing external power to the prosthesis, the power consumption assessment is vital to its commercial acceptance. The average power consumption during a gait cycle was measured to be less than 2.5 W during over-ground level walking, whereas that of a typical active prostheses is in the range of 60–100 W for the same ,42. HencThe level of utilization is one of the parameters to evaluate the usability of any system. A system usability score (SUS)  has beenThere are a variety of factors that affect the applicability, commercial viability, and social acceptability of a prosthesis in many low-income economies. The prosthetic needs in these countries differ based on functional demand, local availability of materials, cost, and cultural barriers . As suggIt may appear that there are seven parameters to tune, but usually in a scenario where the prosthesis is manufactured at a facility and the patient is trained by prosthetist, the current controller parameters can be tuned by the manufacturers and the remaining five can be tuned by the prosthetist. It is justified as the prosthetist is trained and experienced at analyzing gait patterns, and therefore it is easier for he/she to judge the suitable value of the parameters. The highlight of the proposed design is the ease of fabrication in low-resource settings, and thereby can help amputees in locomotion.Though the prosthesis achieves its objective in terms of kinematic performance and other parameters explained earlier, there is ample scope to take it to the next level, as this work is a pilot study. Factors such as heat dissipation, the reliability of the insole sensors, and security of control to prevent falls need further attention. The developed prosthesis design is in its initial stages and further modifications need to be made to bring the design out of the lab setting. A locally manufactured MR damper needs to be fixed to the prosthesis in order to extensively evaluate the performance of the developed prosthesis on a large number of the targeted population as the prosthesis has been tested on single amputee only. A more aesthetic make over with suitable casing and fitting could unveil its humanoid appearance. The present design and control method addresses level walking with a single speed only, a variable speed adaptation would enhance its maneuverability and user friendliness. The future work would include the implementation of the above left-over points with design, development of control scheme for variable speed adaptation, and thorough testing of the modified design on more subjects.A novel low-cost lower limb prosthesis based on sensors placed in a plantar insole was presented in this work. The hardware with suitable control architecture enabled the trans-femoral amputee to attain able-bodied gait kinematics. The results are encouraging and provide a new dimension to the control philosophy of proposed prostheses. The study presents the initial prototype and hence may need thorough testing and related modifications before commercialization. The developed prosthesis is an optimum choice for lower limb amputees in low-income or low-resource settings as well as in developing countries. The attractive features of low cost, durability, and able-bodied gait performance are expected to expand its commercial reach beyond India to other low and medium income developing countries.