A multi-layered interference mitigation approach can significantly improve the performance of Global Navigation Satellite System (GNSS) receivers in the presence of jamming. In this work, three levels of defence are considered including: pre-correlation interference mitigation techniques, post-correlation measurement screening and Fault Detection and Exclusion (FDE) at the Position, Velocity, and Time (PVT) level. The performance and interaction of these receiver defences are analysed with specific focus on Robust Interference Mitigation (RIM), measurement screening through Lock Indicators (LIs) and Receiver Autonomous Integrity Monitoring (RAIM). The case of timing receivers with a known user position and using Galileo signals from different frequencies has been studied with Time-Receiver Autonomous Integrity Monitoring (T-RAIM) based on the Backward-Forward method. From the experimental analysis it emerges that RIM improves the quality of the measurements reducing the number of exclusions performed by T-RAIM. Effective measurements screening is also fundamental to obtain unbiased timing solutions: in this respect T-RAIM can provide the required level of reliability. Time and frequency synchronisation directly impact many market segments. For example, they are crucial for accurate and reliable time-stamping (recording of date and time) in financial transactions and are employed by telecommunication infrastructures and for energy network monitoring. In some cases, the device used for time and frequency synchronisation is installed in remote areas with difficult access to network infrastructures such as fibre optics. In such conditions, a solution can be the use of Global Navigation Satellite System (GNSS) timing receivers, which are easy to operate and have low infrastructural requirements.GNSS is considered an important timing source, which is free, easy to access and highly accurate . The 201Even if illegal, cheap jamming devices are still available in popular e-commerce platforms and an increased number of interference events has been reported in several studies ,4. For iA GNSS receiver for timing retrieval is made of several functional blocks, from the antenna to the final timing solution computation. Thus, several layers of defence can be implemented along the whole receiver processing chain to improve performance in the presence of interference . MultiplIn this work, three layers of defence are considered: at the pre-correlation level exploiting Robust Interference Mitigation (RIM) , post-coWhile a multi-layer approach can significantly improve receiver performance, little analysis has been conducted on the interactions between the different layers of defence. The impact of the mitigation techniques has been assessed in  in the mThe interaction among the different layers of interference mitigation has only been marginally investigated in the literature. Thus, the main contribution of this work is to experimentally evaluate the performance and interaction of these receiver defences with specific focus on RIM and T-RAIM. This paper complements the results presented in . With reThe experimental analysis is based on the setup and the data described in .From the analysis it emerges that pre-correlation techniques enhance the quality of the measurements and reduce the number of exclusions performed by the integrity algorithm. The adoption of multiple layers of defence against interference led to a more continuous and reliable timing solution. In some cases, the receiver was able to provide a reliable timing solution even when the receiver front-end was highly saturated by the interference signal.The remainder of the paper is organised as follows: the different interference defences are described in A schematic representation of the different processing blocks implemented for the evaluation of timing solutions is shown in The PLI can be considered a post-correlation defence whereas T-RAIM performs measurement screening at the navigation/timing solution level.These three layers of defence are described in the following sections.N samples into a transformed domain where interference admits a sparse representation [Pre-correlation interference mitigation techniques operate directly on the samples provided by the receiver front-end. A new stream of samples is produced where the impact of interference should be reduced. The four RIM techniques described in  are consentation . FailureTime Domain Pulse Blanking (TDPB): also known a Pulse Blanking (PB). This technique sets to zeros all the input samples with magnitude greater than a decision threshold, n is the time index. A sampling frequency Time Domain Complex Signum (TDCS): the input samples are processed with the complex signum non-linearity and the output samples are obtained asFrequency Domain Pulse Blanking (FDPB) implements frequency domain excision: the input samples are at first brought into the frequency domain using a Discrete Fourier Transform (DFT)/Fast Fourier Transform (FFT) operation. Blanking is then applied and frequency samples with a magnitude greater than a threshold are set to zero. Finally, the blanked frequency domain samples are brought back in the time domain. In addition, in this case, we used a threshold equal to three times the standard deviation of the frequency domain samples estimated in the absence of interference.Frequency Domain Complex Signum (FDCS): complex signum non-linearity  is appliIn the transformed domain, a non-linearity is applied to reduce the impact of the samples affected by interference. Finally, the samples are brought back into the time domain. Two domains, time and frequency, are considered here along with two non-linearities. In this way, four RIM techniques are obtained. In particular, the following techniques have been analysed:RIM techniques are the most computational demanding approaches among the interference defences considered in this paper. They operate on the digital samples provided by the receiver front-end, which are characterised by a rate which is several thousands of time faster than that of the correlators and of the GNSS measurements. The high data rate of the input samples makes RIM techniques significantly more computationally demanding than the PLI and T-RAIM, which operates on the correlators and the GNSS measurements.N is the DFT/FFT size.Time domain techniques have a linear complexity with respect to the number of input samples. This implies that a fixed number of operations is required for each input sample. The complexity of frequency domain RIM techniques is dictated by the transformation used to bring the sample to and from the frequency domain. When a DFT/FFT is used, the complexity is proportional to A detailed analysis of RIM techniques and of their computational requirements is out of scope of this paper and can be found in  and refeInterference mitigation techniques can also be implemented at the post-correlation level using signal correlators to compute quality indicators, which can be used for excluding measurements. The Carrier-to-Noise power spectral density ratio , W is the weighting matrix which is diagonal and whose elements are function of the z is the vector containing the measurements corrected for all the error terms and for the user-to-satellite distance, which is obtained from the known user position.In this section, the defence layer implemented at the navigation solution level is described. A Galileo-only timing solution is considered. Since it is computed considering the receiver position known, the timing solution is obtained as the weighted mean of the available measurements. Considering the mean as the solution of a least square problem, it can be written as:the C/N0 ; z is thW is diagonal, W and Since his way,  becomesThe residuals, The T-RAIM algorithm implemented in this work exploits residuals: its schematic representation is shown in The different tests used for T-RAIM are briefly discussed in the following sections.The robustness of the geometry is assessed using the TPL which is compared to the Timing Alarm Level (TAL) set to 30 ns for this study.ith element of the row vector:ith diagonal element of the matrixI the identity matrix of size i in ith measurement is obtained by further normalising S.The TPL is composed by two terms, the first related to measurement bias (indicated as Weighted Approximated Time Protected (WATP)) and a second one related to the measurements noise ments in . The WTSFinally, using the maximum WTS, the WATP is computed as :(11)WATPhreshold .K is the protection coefficient depending on the missed detection probability [The noise dependent part of the TPL is computed asbability , and σSoFinally the composite TPL is obtained as:The computed value is compared with respect to TAL; if the TPL is higher than the threshold the solution is declared unreliable because the geometry is not robust enough to support integrity check.If the TPL check is passed, a consistency check on the whole set of measurements is performed; this test is commonly known as GT. The test is based on residuals and the test variable is a weighted quadratic form of the residuals ,22,23:DSC=If A dedicated experimental setup has been designed and implemented to assess the interaction among the different defence layers. The setup is the same one used in  and thusClean GNSS signals were collected using a geodetic antenna placed on the rooftop of an office building in the Joint Research Centre (JRC) campus in Ispra. The antenna was in open-sky conditions and its position was pre-surveyed. In order to contaminate clean signals, a jammer was used. The jammer was placed inside a shielding box whose output was connected to a variable power attenuator used to control the jammer power. Then, the clean GNSS signals and interference signal were mixed using a signal combiner. The output of the signal combiner was connected to a Universal Software Radio Peripheral (USRP) used to collect In-phase Quadrature (I/Q) samples that were stored on disk. Using a customised Matlab software receiver the samples were processed several times, applying the techniques described in PLI on and T-RAIM off (PLI);PLI off and T-RAIM on (T-RAIM);PLI and T-RAIM both on (PLI + T-RAIM).The first layer of defence considered in this paper is represented by RIM techniques at the pre-correlation level, hence five baseline configurations  can be considered including four RIM algorithms and the case without mitigation. Then for each of these five baseline configurations, three cases are considered:number of satellites used in the timing solution;TPL, in particular the variation of the TPL is considered;maximum residual;frequency stability, evaluated using the overlapping Allan Deviation (ADEV) ,26.The cascade effects of the mitigation techniques are assessed considering:In this section, the results obtained considering Test 1 are presented. In order to avoid the repetition of a similar finding, only results relative to Galileo E1 are presented. Results related to GPS L1 Coarse Acquisition (C/A) signals can be found in .The first parameter assessed is the number of satellites used in the timing solution; in order to evaluate the impact of the different defence layers for each configuration, the variation of the number of satellites with respect to the baseline configuration is shown in In all tests considered, an increasing jamming power was progressively injected . During In the final part of the test, for the configurations with PLI and T-RAIM, the exclusions are mainly performed by the PLI, which reduces the satellite availability. With a low measurement redundancy, T-RAIM is unable to perform additional exclusions.For an easier comparison among the different configurations with both, PLI and T-RAIM activated the variation of used satellites, which is shown in “No mitigation” and FDCS cases have similar performance, while time domain RIM reduces the number of excluded satellites leading to a reliable timing solution for almost the entire test duration.Statistical parameters, mean and maximum value of the variation of the number of satellite used in the timing solution and for Test 1 are reported in The TPL has been computed for the all the configurations (including the ones without T-RAIM) and its variation with respect to the baseline configuration is shown in The TPL varies according to the satellite availability: larger and earlier increases are observed for the FDPB cases, while the FDCS and “no mitigation” cases have a similar behaviour. Finally, time domain RIM techniques show an increased TPL (up to 20 ns) only in the very last part of the test.The TPL variation is shown in The maximum value of the residuals is shown in In order to analyse the impact of the pre-correlation techniques, the maximum value of the residuals as a function of time is shown in The ADEV of the timing solutions is shown in For the configurations with T-RAIM activated, the ADEV curves have been computed using only reliable epochs; for the configurations without T-RAIM, the ADEVs have been estimated considering either all epochs (light blue and green lines) or considering reliable epochs only. When only reliable epochs are considered, the curves are very close to each other and only small differences can be noted for averaging time interval lowers than 3 s. Completely different curves are obtained considering all the epochs showing the impact of removing unreliable solutions.The ADEVs curves using RIMs show that all the configurations converge to similar values for averaging time intervals larger than 5 s while, for In this section, the results of Test E5B using Galileo E5Bsignals are presented.In In order to better compare the performance of the different techniques, the five cases are jointly analysed in Mean and maximum values of the variation of the number of satellite used in the timing solution and for Test E5B are reported in In The configurations with PLI and T-RAIM are compared in Mean and maximum values of the variation of the TPL are reported in The maximum residual value is shown in The impact of the different pre-correlation mitigation techniques on the maximum residual value is analysed in The impact on the stability of the timing solution is analysed using the ADEV; the curves for the configuration without RIM techniques are shown in the left part of In the right part of In this paper, an experimental evaluation of the performance and interactions of three levels of receiver defences against jamming and interference has been performed. The work focused on RIM, PLI and T-RAIM. While the first layer of defence at pre-correlation level, e.g., RIM, significantly improves receiver performance, a further screening of the measurements is required to obtain reliable timing solutions. PLI and T-RAIM complete the work performed by RIM leading to reliable timing solutions.The performance has been evaluated using real GNSS signals mixed with an increasing level of interference: the tests presented are performed on two frequencies (E1 and E5B) to demonstrate that the proposed approach is suitable for different frequencies and modulations.From the analysis it emerged that a proper mitigation technique at pre-correlation level improves the reliability of the measurements leading to a reduced number of exclusions performed by the FDE algorithm. For the type of jammers used in the tests, time domain RIM is the most effective approach leading to a reliable timing solution for almost the entire duration of the test. When the interference level is very high, the exclusions are mainly performed by the PLI which reduces the satellites availability. With a low measurement redundancy, T-RAIM is unable to perform additional exclusions.In a multi-layers approach the proper selection of the mitigation techniques is fundamental to avoid performance degradation in the presence of jamming.


Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity. Several epidemiological studies provided a strong correlation between physical work exposures and the increased risk of work-related musculoskeletal disorders (MSDs) ,2,3,4. RBiomechanical exposures during physical work are mainly due to three main factors: intensity , repetition (frequency or number of force exertions and motions) and duration  .In addition to the more traditional quantitative or semiquantitative observational methods ,11,12,13In occupational ergonomics, body-worn inertial sensor technology and motion tracking system could be combined to noninvasively collect large amounts of body movement data during physical work  and explAmong the activities involving biomechanical overloading, material handling and lifting is one of the most studied in the scientific literature, including its association with the development of work-related MSDs . With a In the scientific literature, among the many applications of wearable technology to ergonomics, and in particular among those which use the NIOSH methodology , the assMoreover, machine learning (ML) algorithms are gaining popularity in the ergonomic field for biomechanical risk assessment by means of data acquired by wearable inertial systems. Several publications have appeared in recent years documenting several strategies ,28,29. IThe question remains whether it is possible to classify lifting tasks belonging to different risk classes according to the value of LI using a machine learning approach by means of features extracted from raw signals.The aim of this study is twofold: First, we explored the possibility to use a single IMU placed on the lumbar region to monitor the biomechanical risk. Second, we assessed if the time-domain features extracted from acceleration and angular velocity signals acquired by the IMU sensor allowed to classify risk/no-risk tasks according to the NIOSH methodology.To classify lifting tasks belonging to different LI classes according to the Revised NIOSH Lifting Equation (RNLE), we fed several ML algorithms using time-domain features extracted from acceleration and angular velocity signals. The signals relating to the lifting activities were acquired through the wearable Opal System. The validation of the methodology was carried out through the tenfold cross-validation and different evaluation metrics in order to make the result more robust.The Opal System by APDM Inc. is the commercial IMU-based wearable system employed in the current study a. The syThe RNLE is a method published by NIOSH to assess the risk of work-related low back disorders (WLBDs) . An equaLI, namely the ratio of actual weight to RWL for lifting activity, is a good indicator of the risk of WLBDs for manual lifting ,41,42.In the preventive approach, RNLE is useful for verifying that LI is not greater than 1 in lifting activities. Lifting activities with LI values between 1 and 3 are associated with an increased biomechanical risk in the working population, while lifting activities with LI less than 1 indicate acceptable conditions and no risk of WLBDs .LC: Load Constant = 25/20 kg , 20/15 kg ;HM: Horizontal Multiplier;VM: Vertical Multiplier;DM: Distance Multiplier;AM: Asymmetric Multiplier; andFM: Frequency Multiplier.The RWL equation is as follows:Healthy volunteers were recruited from the bioengineering laboratory of the University of Naples “Federico II”. Subjects were included if they were between the ages of 20 and 60, and excluded if they had hypertension , or any other heart disease , or a musculoskeletal disorder . Seven subjects were selected for this preliminary study, whose anthropometric characteristics are shown in Each subject performed a task session based on two trials. Each trial consisted of 30 consecutive lifting tasks. Between the two trials, a pause of 1 h was considered. Specifically, the first trial consisted of repeated liftings in a condition of LI less than one (LI = 0.5) named the NO RISK class, while the second one consisted of repeated liftings in a condition of LI greater than one (LI = 1.3) named the RISK class. LI of 0.5 and 1.3 were derived from the RNLE  by vario3) with weights equally distributed inside; the size of the plastic container is such that it can be held close to the barycentre of the body during the lifting task. The subjects were instructed to adopt a stable upright posture with the lower limbs slightly apart and to perform the squat technique with a two-handed grip. Each lifting task was performed in a slow, controlled manner without any jerk or sudden acceleration [The trial  was perfleration . The resroot mean square (RMS),standard deviation (SD),minimum (MIN) andmaximum (MAX).Acceleration and angular velocity signals were not filtered . The sigAs during the lifting activity the subject moved in two different directions , giving rise to positive or negative variants of the signals, it was considered that RMS represented the potential differences in the two different study conditions (LI < 1 and LI > 1) better than the average. SD captured the fact that the range of possible acceleration and values of the angular velocity signals can differ for different LI values, as the SD of the acceleration data was associated with the intensity of motion . Both RMSeveral ML algorithms were trained to evaluate the performance of our predictive model based on features in the time-domain extracted from raw signals (acceleration and angular velocity) to discriminate two risk conditions according to the RNLE.The following ML algorithms were implemented.Random Forest (RF) is a ML algorithm based on bagging and randomisation. RF uses several decision trees. An instance is assigned to a certain class through a majority vote procedure on the basis of the decision given by the trees that make up the forest. Although this type of ML shows optimal performance on large datasets, it was chosen in this study for its simplicity of parameterisation and for its stability to noise and outliers .Decision Tree (DT) is a hierarchical classifier method, as well as representing the simplest and most used logic-based classification technique . The tesGradient Boost (GB) redefines boosting as a numerical optimisation problem with the goal of minimising model loss of function by adding learning weaknesses and using gradient descent to set the local minimum of the differential function . The GB The AdaBoost (AB) algorithm , short fk-Nearest Neighbour (kNN) ranks the unlabelled instance vector on the basis of the majority label class between its nearest k neighbours in the training set. Several distance metrics, employed to recognise the nearest neighbours, are present in the literature and can influence the algorithm performance. Without prior knowledge, typically the distance metric implemented is the Euclidean distance , as in tNaive Bayes (NB) is a probabilistic ML algorithm, based on Bayes’ Theorem. It calculates the probability of each class for a specified instance and then returns the class with the highest probability. This algorithm, which requires little data for training and little storage space, is suitable for the small size of the datasets that perform the analysis for each subject in our study. It is also fast during training without many parameters to set, based on the assumption of the conditional independence of the characteristics .Multilayer Perceptron (MLP) consists of multiple layers of simple, two-state sigmoid processing elements, i.e., nodes or neurons interacting using weighted connections, only between neurons of adjacent layers . In the Support Vector Machine (SVM) in a binary classification, as in the case under study, creates a hyperplane that separates data from two different classes. The largest possible distance is established between the separating hyperplane by maximising the margin, thus creating the separation . The kerLogistic Regression (LR) is an efficient and powerful way to analyse the effect of several independent variables on a binary outcome, as in the case under study, and allows quantifying the contribution of each feature. LR iteratively identifies the strongest linear combination of variables with the highest probability to detect the observed outcome .K-fold cross-validation is one of the most widely used approaches for estimating classifier error and was employed in our study as tenfold cross-validation (CV) to validate the predictive models described above and provide more robust evidence on our predictive biomechanical risk model, based on features extracted from raw signals. Tenfold CV involves splitting a dataset into ten subsets, with the iterative use of nine to train the model and one to evaluate its performance . In thisMoreover, in order to better generalise the validation of our proposed approach, we used a leave-one-subject-out cross-validation using six subjects to train the predictive models and one subject to test the algorithms.The performance of the predictive models proposed was evaluated through the following evaluation metrics: Accuracy, Sensitivity, Specificity and Area under the curve Receiver operator characteristic (AucRoc). The Accuracy metric is a measure of the ratio of correct predictions over the total number of instances considered. The Sensitivity metric is employed to measure the fraction of positive patterns that are correctly classified, while the Specificity metric is used to measure the fraction of negative patterns that are correctly classified. Finally, the AucRoc reveals the generally ranking performance of a classification algorithm.The Confusion Matrix of the best algorithm in terms of evaluation metrics scores was also reported, with matching between instances in an expected class and an actual class .Finally, the Feature Importance was also reported through the calculation of the Information Gain (IG), an indicator of the amount of information provided by the features . A featuML algorithms have been implemented through the artificial intelligence platform Knime Analytics Platform (version 3.7.1), which finds increasing diffusion in the scientific literature ,63.First, we performed a ML analysis for each subject to assess the feasibility of the proposed data mining system to assess the biomechanical risk for a single subject. For each subject, we considered two datasets: the first dataset is composed of 60 instances, 30 for each class , and 12 features extracted from the acceleration signals; the second dataset is composed of 60 instances, 30 for each class , and 12 features extracted from the angular velocity signals. For each dataset, we performed the ML analysis by averaging the results among the seven subjects, and we further showed the standard deviation in order to include prediction uncertainty. The results for each dataset are shown in Second, we performed Feature Importance by means of the calculation of the IG, considering the entire study sample and the features extracted from both the acceleration and angular velocity signals along the three axes .Third, we performed a ML analysis considering all seven subjects to assess the feasibility of the proposed data mining system for biomechanical risk assessment for a general study population. In this analysis, we considered a unique dataset consisting of 420 (60 × 7) instances, 210 for each class , and 18 features extracted from both acceleration and angular velocity signals excluding the six features with IG equal to zero . In our The results of the ML analysis on the entire dataset are shown in Finally, we performed a ML analysis on the entire dataset using as validation strategy the leave-one-subject-out, namely, using six subjects for the training set and one subject for the test set. Results are shown in the The goal of our research was to explore the feasibility of several state-of-the-art ML algorithms—fed with specific time-domain features extracted from the acceleration and angular velocities signals during a lifting activity—to classify the lifting risk classes based on the LI values according to the RNLE. The results obtained suggest ML algorithms—operating on the time-domain features  extracted during lifting activities from the acceleration and angular velocity signals along the three dimensions of space—can offer valid help to experts in ergonomics for the precise and automatic classification of the biomechanical risk of workers engaged in load-lifting activities.The ML analysis performed was aimed at the classification of lifting activities based on the presence or absence of risk, defined by the LI index.First, having carried out a ML analysis for each subject, we obtained the average scores and the standard deviations of the evaluation metrics of the seven subjects by considering separately the characteristics extracted from the acceleration signal and the angular velocity signal. As shown in y axis, i.e., the mediolateral direction  according to NIOSH using a single IMU placed on the subject’s pelvis to extract four basic time-domain features. The achieved results, when compared with the recent ones described by other research groups, are in line or superior to those based on more complex methodologies.In the study by Varecchia et al. , the comSnyder et al. ,69 propoWith a similar goal to ours, Brandt et al.  tried toAlthough the presented methodology is powerful, doubts could be raised about the effective capabilities of a single IMU for the validation of such results. Although a single IMU on the pelvis is not sufficient to fully predict the parameters associated with lifting , this solution estimates the lumbar load fairly well, when the displaced mass is known and is in a consistent position with respect to the body . In addiBased on these results, the experimented approach—which combines time-domain features and machine learning algorithms—proved to be a valid indicator, although preliminary because of the low number of samples analysed, of the risk of WLBDs for manual lifting (according to the NIOSH index) to which workers are potentially exposed during their working activity.In conclusion, the results showed that the proposed combinations of features—extracted from acceleration and angular velocity signals acquired by a single IMU placed at pelvis—and state-of-the-art ML algorithms represents a potential valid approach to automatically classify the biomechanical risk to which subjects may be exposed during lifting activities, for example, at work. The presented methodology could represent a valid integration to the established protocols  to evaluate the biomechanical risk more quickly and easily. Moreover, it could represent a valid alternative when the conditions required for the application of standardised evaluation methods do not exist. These results are of direct practical relevance for occupational ergonomics, as they present the opportunity for automatic, economic and non-invasive detection (by placing an IMU on the pelvis of the subjects), to assess the musculoskeletal and biomechanical risk associated with lifting. Based on the promising results presented in this article, the next phase of our research could consider an extension of the sample under consideration to further validate the methodology, new experiments that consider more than two LI values, and the integration of additional features from the time-domain and possibly the frequency-domain. The main limitation of the study is in fact the low sample size making this a preliminary study. Future work will benefit both from the inclusion of larger groups of participants as well as the use of deep learning techniques for classification such as convolutional neural networks . It is k


Tactile sensing endows the robots to perceive certain physical properties of the object in contact. Robots with tactile perception can classify textures by touching. Interestingly, textures of fine micro-geometry beyond the nominal resolution of the tactile sensors can also be identified through exploratory robotic movements like sliding. To study the problem of fine texture classification, we design a robotic sliding experiment using a finger-shaped multi-channel capacitive tactile sensor. A feature extraction process is presented to encode the acquired tactile signals (in the form of time series) into a low dimensional (≤7D) feature vector. The feature vector captures the frequency signature of a fabric texture such that fabrics can be classified directly. The experiment includes multiple combinations of sliding parameters, i.e., speed and pressure, to investigate the correlation between sliding parameters and the generated feature space. Results show that changing the contact pressure can greatly affect the significance of the extracted feature vectors. Instead, variation of sliding speed shows no apparent effects. In summary, this paper presents a study of texture classification on fabrics by training a simple k-NN classifier, using only one modality and one type of exploratory motion (sliding). The classification accuracy can reach up to 96%. The analysis of the feature space also implies a potential parametric representation of textures for tactile perception, which could be used for the adaption of motion to reach better classification performance. Tactile sensing is fundamental for robots to understand the space surroundings by revealing some contact features not directly accessible to visual and acoustic sensors, including pressure, vibration, and temperature. Tactile sensors are specifically designed to convert the instant changes of these physical properties into electrical signals. Unlike visual and acoustic sensing, tactile sensing involves and probably only involves the direct mechanical interaction between the sensor and the object in contact with. In the majority of the cases, external environmental conditions like illumination, acoustic noise, humidity, and temperature do not affect the capability of tactile sensing. Despite its robust performance in different scenarios, tactile sensing is a very limited instrumental modality that only captures the regional stimulus around a sensor. Luckily, the limitation can be alleviated by pairing the sensing with exploratory robotic motions to enlarge the contact area, requiring both spatial and temporal decoding to interpret the signals. The process of decoding signals to comprehend the space surroundings is the core of tactile perception. Unfortunately, at the current stage, there is no such a uniform and standard format of tactile sensor and tactile data and hence tactile perception is tightly bonded to the specific sensing technology being used  with non-destructive methods.To classify fabrics, usually, active motions are necessary to acquire a holistic tactile sample of the texture since tactile sensors only capture regional stimuli. Manfredi et al.  found thSince tactile decoding and perception are tightly bonded with the specific sensors being used, one of the very first objectives is to construct a robust perception system that could extract certain tactile features from the tactile signals. The tactile features are desired to embed some peculiar information to the fabrics, independent of the variation of sliding parameters during the acquisition stage. The study Weber et al.  on the tvia sliding, performance can be improved by simply adjusting the motion parameters.In reality, it is ideal to have the capability of adapting the robotic behavior to compensate for the limitation of the sensing technology  and the perception algorithm since the mechatronic system itself is usually unmodifiable. Adaptation first requires an overall understanding of correlations between motions and tactile signals. Our study aims to give a general picture so that in the task of fabric classification We will also testify to the expansibility and scalability of the algorithm. Expansibility suggests that the algorithm applies to textures other than fabrics on the classification task, while scalability means that the system can incorporate tactile information from new fabrics in an iterative approach.Beyond the classification task, another purpose of the research is to search for a potential parametric representation of the textures in the feature space that can be used further in a more complex system for fabric handling and manipulation.via sliding and rubbing fingers on the fabric surface, we command a robotic arm equipped with a capacitive tactile sensor on its end-effector to grip the fabric and slide. Different from some existing attempts of texture identification in Fishel and Loeb  and the quality of these features are self-explanatory.This section details the signal decoding and perception algorithms. We describe the pipeline of signal processing and feature extraction that maps the original tactile signals in large matrix form to low dimensional vectors and introduce a weighted k-NN classifier to identify the fabrics in the feature space.taxels  that only perceive local stimuli and generate multi-channel signals over time. We follow a feature extraction method based on incremental principal component analysis (IPCA) to gradually extract the frequency features during the process of sliding and touching different types of fabrics. The feature extractor first transforms a tactile time series into a multi-channel frequency spectrum in the format of matrix and resamples the frequency spectrum to a fixed size. After that, the frequency spectrum (as a matrix) is vectorized (flattened). After collecting multiple tactile measurements and transforming them all to resampled, vectorized frequency spectra, we stack them together to form a large data matrix. Then, we apply IPCA transformer to project the data matrix to lower-dimensional vectors. With the condensed representation of tactile measurements, it is possible to classify fabric textures by training a k-nearest neighbors (k-NN) classifier.A tactile sensor usually consists of several X, represented in the matrix formA tactile measurement, M-channel time series is first normalized toPressure settings into the same scope such that comparative analyses are directly available. The mean-deviated and scaled tactile measurement is then transformed into the frequency domain by applying Fourier transform channel-wise, taking only the magnitude to gain the real frequency-spectra matrix Y defined byusing the channel mean and SD ya(b) is given bywhere each entry Nr is a predefined resolution constant, is then vectorized (flattened) into a frequency-spectra vector Resampling is necessary here to unify the sizes of different spectra as the scopes of the frequency spectra are dependent on the length of the original time series, which can vary among measurements since the experiments are conducted with several different sliding speeds. The re-sampled frequency matrix Multiple tactile measurements acquired in sliding motions, as vectors of the same dimension now, can be stacked together to form a new observation matrixK is the total number of measurements. We then resort to principal component analysis (PCA) on the observation matrix O for dimensionality reduction and feature extraction.containing all the frequency vectors, where O.Incremental principal component analysis as an unsupervised method is well suitable for dimensionality reduction in our problem. It preserves as much as possible information contained in the original data matrices by minimizing a reconstruction loss. We apply the IPCA introduced in Ross et al.  to fit tO as Ô. The goal is to find a feature matrix Q ∈ ℝD×K with D ≪ NrM such that the total reconstruction errorDenoting the mean-deviated form of is minimized. Frobenius norm is chosen considering its fast and easy computation, while other similar matrix norms also function the same for this optimization setup. Here, n new measurements pre-processed and vectorized, formatted as a matrixGiven n new data requires the computation of singular-value-decomposition (SVD) for the mean-deviated form of the augmented data matrix OK+n = [O A], which is not ideal for online applications. In the presence of more tactile measurements, the data matrix keeps expanding, and traditional PCA will slow down drastically.A brutal update for these Incremental principal component analysis differs from traditional PCA in handling new data. Instead of re-computing the SVD for the entire augmented data matrixA, only the SVD of the horizontal concatenation of the original and the additional data matrix, and one additional vector K+n, first we definewhere Â is the mean-deviated form of and computeorth performs orthogonalization andwhere via QR decomposition R as V′ is not directly used in IPCA, it is not calculated explicitly.Considering that tactile measurements are acquired incrementally, the feature extractor can be trained upon known data. During the procedure where more new tactile measurements are presented the IPCA based feature extractor can first map the new data matrices to feature vectors to perform classification, and then partially fit the newly sampled data to incorporate the information and improve the performance. The application of the incremental method enables the feature extractor to adapt to the growing database fast and efficiently.D-dimensional feature space using distance-weighted voting by its k nearest points.In the lower dimensional feature space, a weighted k-NN classifier Dudani,  can be fk nearest points to the query point Given a feature vector I is the indicator functionwhere Our research employs a capacitive tactile sensor CPM-Finger  is the distance between the two plates as a function of the applied pressure P. At the sampling rate of 32 Hz, the sensor signals in 1 s can be arranged into a matrix F and carry the values around for simplicity.where ϵ is the permittivity of the dielectric middle layer, The sliding experiments are implemented with a Franka Emika Panda 7DOF robotic arm with a two-finger gripper as the end-effector. One CPM-Finger tactile sensor is installed on the gripper to replace the original rubber fingertip.BeigeCotton, BrownCotton, Linen, Canvas, Denim, DenimFlex, and WovenFabric as shown in In total, seven types of fabrics are used for the experiments. We name them as It is not always possible to measure the exact tension of the fabrics in real applications given the complex forms of the fabrics;Non-uniform tension among the fabrics can serve as a testimony of robustness of our methods;Due to the friction between the protective fabric Spandex and the inspectee fabric, stretching and twisting happen during sliding in a hard-to-predict way, preset tension has a little indication to the results, especially for higher sliding pressure settings.via proportional–integral–derivative (PID) control on the closing distance between the fingertips. We capture the average value of the 16 sensor readings as an indicator of the grip pressure (Pressure).The robot gripper is controlled to grip the fabric stripe with constant pressure and slide vertically at a constant speed to collect one set (3 samples a set) of tactile signals for each parameter setting (sliding up and down in the same velocity are considered as two speeds). The grip pressure is maintained The control command is computed and sent to the robot host controller through a Linux OS patched with a realtime kernel.The data acquisition proceeds as follows:Pressure is reached, and holds the Pressure. The robot closes the gripper till the desired  The robot moves the gripper vertically with a constant speed downward and then upward for a distance of 50 cm, respectively, at the same velocity. The tactile signals as time series are captured during the process and stored in the format of a matrix. Each pair of sliding parameter settings is repeated 3 times. Then, the robot releases the gripper, moves horizontally away from the current tested fabric stripes, and shifts to the next fabric, till all fabrics are tested with the current sliding speed and holding pressure.Pressure, as listed in The robot repeats the whole sliding experiments from steps 1 to 3 with different pairs of control parameters, i.e., speeds and Denim and DenimFlex in our nomenclature, skip through the sliding with Pressure 250, and WovenFabric passes through both Pressure 210 and 250, as in these cases, the torques required to conduct the sliding motions exceed the maximum payload of the robot due to severe folding and twisting of the inspectee fabrics caused by frictional force. With all the other available Pressure settings, sliding motions in all 6 speeds are executed. In total, 966 samples are collected as matrices in the shapes of N × M where N is dependent on the duration of the sliding motion and M is the number of signal channels, i.e., the number of taxels, which is 16 for our CPM-finger sensor.Two types of denim fabrics, i.e., To simulate the scenario where new fabric classes are presented, we follow an iterative process to update the feature extractor and test the classifier:Canvas and DenimFlex as prior knowledge, i.e., initial training classes, to fit the IPCA feature extractor.We first randomly select two fabrics, e.g., The measurements from the two training classes are transformed into the feature space by the feature extractor just trained on them. Tactile measurements of all the other (unfitted) fabric classes serve as the test dataset. They are transformed into feature vectors to testify to the classifier.Randomly pick one unfitted fabric class as a newly presented class to update the IPCA feature extractor with a partial fitting method. Add the class to the training classes.Project the data of training classes into the feature space.Repeat from 3.Canvas and DenimFlex, in the 3D space . We first show the confusion matrix  at a time. With the same feature extractor trained on D = 3 PCs, the classifier shows a performance fluctuation under different Pressure settings  stretched or even over-stretched condition of the fabric stripes, where fabrics textures are severely distorted or even flattened out. Whether a fabric stripe is stretched enough for identification and classification might as well be closely related to the resolution and sensitivity of the tactile sensor itself. As can be seen in Since only 4 fabrics can be sampled under F sequentially, in the ascending order. Data points are more scattered under larger pressures, which confirm our conclusion in the last paragraph that larger holding pressures help to extract more information from the fabrics. However, another notable phenomenon is that features are also less consistent (more scattered) under larger pressures. This is very likely caused by the very strong interaction between the sensor and the fabric stripes, where the sliding motion is not as smooth as it is under lower pressure settings due to augmented frictional forces. In the experiments, due to gravity, anisotropic “fingerprint” of the sensor and fabric folding and shifting, the sensor stutters during the sliding motion.To better illustrate the effect of grip pressure, we show in Finally, we show the effect of different sliding speeds on classification in signature of a fabric texture but also embeds some characteristics of the tactile sensor itself. Even at a relatively low sampling frequency of 32 Hz, we can still reach a considerable classification accuracy of 96% for 7 fabrics by using a 7D feature extractor.Given the results shown above, mostly the visualization of the feature space with clusters and the analysis of classifier performance concerning the parameters of sliding motions, we make a statistically sound inference that a low dimensional (circa ≤ 7D) vector is sufficient to feature the tactile measurement of textures sampled from our CPM-Finger tactile sensor. The feature vector is essentially a condensed form of the frequency spectrum which not only represents the frequency The fact that the same processing pipeline has significantly different performance on fabric classification when varying sliding motion parameters, supports our assertion (see Section 1) that, tactile sensing as a contact perceptive technique, is conceptually very different from the visual and auditory perceptions. Tactile sensors capture the information in the interaction with the environment, during which the interactive modes  and parameters are also reshaping the environment, that reversely affect the tactile measurement itself. Whereas for visual and auditory perceptions, movements of the sensory system are not mandatory to acquire signals and have no direct impact on the observable most of the time.For a specific sensing technology, varying the parameters of the exploratory motions not only serves to enlarge the perceptive field and gain more information but also helps to seek the best interactive conditions of the tactile sensors regarding the object. The essence of tactile sensing is a capture of the generated deformation during the mechanical interaction between the sensor and the object surface. Changing the motion parameters for better classification performance can be viewed as a robotic adaptation to maximize the efficacy of the sensors and the perception system, given that specs of the sensors  and the physical properties of the object are likely unalterable.In this study, we focus on the classification task of only fabrics. However, it is natural to question whether the same methods apply to the classification of general materials. Some preliminary results of the experiments on a 3D printed polylactic acid (PLA) board with two types of Boards  with no more than 7D feature vectors. Tactile measurements represented by points in the low dimensional frequency feature space are segregated by their fabric classes naturally. These clusters can possibly be depicted by parameters of probabilistic models, e.g., Gaussian mixture, to form a more compact representation of tactile knowledge base. This implies the great potential of tactile sensing in other tasks relating to object identification and classification.https://github.com/wngfra/FabricSlide.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number can be found at: GitHub, SW performed the work and contributed to the writing of the manuscript. AA contributed to the writing of the manuscript. GC provided research supervision, and contributed to the manuscript revision, and read and approved the submitted version. All authors contributed to the conception of the project.The research was entirely supported by the University of Genoa.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability.This study proposes three algorithms that, given a convenience sample and variables relevant for the outcome of the study, select a subsample with specific distributional characteristics, boosting either representativeness (Probability and Distance procedures) or risk factors’ balance (Uniformity procedure). A “Quality Score” (QS) was also developed to grade sampled units according to data completeness and reliability.The methodologies were evaluated through bootstrapping on a convenience sample of 135 hospitals collected during the 2016 Italian Point Prevalence Survey (PPS) on HAIs.The QS highlighted wide variations in data quality among hospitals , with most problems ascribable to ward and hospital-related data reporting. Both Distance and Probability procedures produced subsamples with lower distributional bias (Log-likelihood score increased from 7.3 to 29 points). The Uniformity procedure increased the homogeneity of the sample characteristics .The procedures selected hospitals with higher data quality, especially the Probability procedure (lower QS in 100% of bootstrap simulations). The Distance procedure produced lower HAI prevalence estimates (6.98% compared to 7.44% in the convenience sample), more in line with the European median.The QS and the subsampling procedures proposed in this study could represent effective tools to improve the quality of prevalence studies, decreasing the biases that can arise due to non-probabilistic sample collection.The online version contains supplementary material available at 10.1186/s12874-021-01277-y. Healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) have been widely recognized as a significant threat to public health, with an estimated prevalence in acute care hospitals of 5.9% and an annual incidence of 3,760,000 new cases per year . TherefoThe European Union is on the frontline in monitoring and controlling HAI risk in its Member States, under the coordination of the European Centre for Disease Prevention and Control (ECDC) healthcare-associated infections surveillance network (HAI-Net). This network has carried out two European-wide Point Prevalence Surveys (PPSs), applying a standardized protocol developed by the ECDC, in 2011–2012 and 2016–2017  to account for non-linear monotonic relationships; the effect size of this relationship was estimated through lognormal regression and the QS Ratio [95% CI] for a 100-beds increase is reported. The relationship between QS and hospital HAI prevalence was estimated using Spearman correlation and the effect size through quasi-logbinomial regression. A quasi-likelihood model was chosen to account for the additional variance derived by the fact that patients are clustered into hospitals and therefore not independent one from another  as insteBoth correlation and regression analyses were performed on the entire dataset and after stratification in 3 groups according to the hospital size , to highlight possible non-linearities in relation to this parameter.Nrequired = 55, were generated respectively by the Probability, the Uniformity, and the Distance (G and S) procedures. In addition, another subsample A simulation approach was adopted to evaluate the characteristics of the subsamples produced by the Distance, Probability, and Uniformity methods. The original convenience list was resampled by bootstrapping  2000 timLog-likelihood fit: a Bayesian regularized logistic regression model with weakly informative Cauchy priors with mean zero and scale of 2.5 standard deviations , 38.2%  and 18.2%  of small, medium and large hospitals respectively . The QS was sensibly lower in the subsample generated using the Probability procedure, which is expected since its purpose is to reweight the probability of a hospital to be selected. Despite having a stronger influence compared to the other methods, the impact of the QS was still 7.7 times inferior to the impact of size/location in determining the final inclusion probability in the sample .Notably, none of the procedures significantly impacted the estimated HAI prevalence Table . The larNotably, the bootstrapped 90% intervals of both the baseline and the average changes in the prevalence estimates, in all procedures, showed a range of possible values broader than two percentage points, indicating that with such small sample sizes, wide differences between prevalence studies can be justified simply by random variation due to sampling.Using the 2016 Italian PPS on HAIs as a case study , we developed a set of subsampling methodologies to improve the representativeness of epidemiologic studies when the selection of participating units is made using non-probabilistic strategies, such as convenience sampling.Convenience sampling  is one oThe major drawback of this methodology is that the selected units may not be representative of the target population, introducing distortions in the distribution of sample characteristics, and in the worst case scenario, also in the outcomes of interest , 22, theFor country-level, clustered prevalence studies, convenience sampling may be simpler and more cost-effective compared to more formal sampling strategies, such as systematic sampling (as suggested by the ECDC PPS protocol), since it does not assume the compliance to participation for all (or most) of a country’s hospitals. Employing systematic sampling, for example, may be difficult when a central selection of participating hospitals is not possible, due to the specific organization of the healthcare systems  or to non-willingness to enforce compliance. In Italy, regional authorities are virtually exclusively responsible for healthcare organization and delivery, within a framework provided by the central Government.It is customary in these cases to select participating units for example among hospitals which are part of some established surveillance network , or to lAs previously mentioned, the Italian PPS sample was generated by convenience sampling driven by regional health authorities, and this generated issues in terms of geographical and risk factor distribution. Therefore, this sample represented a perfect case for testing our methodologies, which try to measure the impact of these issues and reduce them by algorithmic subsampling.First, we developed a score to quantify the quality of the collected data (QS) in terms of missing data and errors. In statistical practice, there is a significant corpus of literature on the negative impact of missing data on inference –29, espeThe issue of errors in the collected variables is equally relevant: they can introduce distortions in the resulting estimates and statistical associations, which are very hard to identify and account for at the analysis stage –34.The score we propose considers both the amount of missing information and possible errors in data collection, weighted by the statistical relationship of the considered variables with the primary outcome of the study, i.e. the risk of HAI. This score can be used to rank and select statistical units based on the reliability  of their data, thereby reducing the reliance on analytic solutions like imputation and favoring the use of the original data. The consequence is a more transparent analytic pipeline.While applying the score to the 2016 Italian PPS data, we observed a large gradient of values, the hospitals with worse data scoring 78.7 times higher  than the one with the best (lowest) score. The analysis of the components of the score can help identify which variables have more issues: in our case, the information at the patient level was more complete than ward and hospital-level characteristics. These findings could be useful for the conduction of future surveys, by highlighting problematic variables that should be addressed when training local operators regarding data acquisition, and by revealing issues in the definitions of variables that could make their collection problematic.When we compared the QS with HAI prevalence, we observed a slight negative association, that is, worse data quality predicted lower HAI prevalence. The relationship, albeit weak, was conserved after stratifying hospitals by their number of beds, a known predictor of HAI risk in hospitals . These rThe second problem we tried to solve was the possible bias in the representativeness of hospitals chosen by convenience sampling (or other non-probabilistic sampling strategies). A common approach to the problem of under/overrepresentation is based on the reweighting of observational units: for example weighting observations according to the inverse of the probability of being selected to reduce disparities between subgroups  or basedOur approach is based on two subsampling techniques (Probabilistic and Distance procedures), which, informed by country-level information, subsample hospitals generating a distribution more similar to what would have been achieved by random sampling, given a set of stratification variables. As reported in the methods, we selected geographical location and hospital size as characteristics of interest, both because these are known risk factors for HAI and increased AMU , 12 and The Probability procedure uses country data to build a probability distribution given one or more variables and chooses hospitals according to it. The Distance method defines strata using the same distribution and then fills them with the hospitals in order of similarity to the strata according to a hierarchy of hospital variables: if units with the right characteristics are not available, the model selects hospitals which are as similar as possible. Finally, it further refines the sample by randomly switching hospitals in and out, updating the sample only if the switch improves the distributional fit. It can be associated to a greedy gradient search followed by a random search phase to escape possible local maxima.We further proposed a Uniformity sampling method providing a balanced sub-sample for the considered characteristics, which may be useful for risk factor analysis and prediction models .The QS is considered in all methodologies, selecting the best hospital among equivalent proposals in term of distributional fit.Based on our dataset, both the Probability and the Distance methods sensibly decreased the distributional bias of the generated subsamples, compared to the convenience sample. This improvement highlights the possible drawbacks of non-probabilistic sampling methods and supports the necessity of adjustment before analysis. Nevertheless, it should be noted that the specific subsampling algorithm only had a relatively small effect on the final estimates of HAI prevalence, which ranged from 6.98% for the Distance method prioritizing hospital size (S), to 7.87% for the Probability method, compared to 7.44% for the convenience sample estimate. As reference, the HAI prevalence at the European level, as reported by the ECDC PPS study, was 5.9% . The DisThe variability of the bootstrapped estimates was large: all methods showed a 90% BI of more than two percentage points. This difference is quite significant in terms of burden of disease: it is greater than the variation in HAI prevalence observed among most of the countries in the European PPS study or in the same country but in surveys conducted in different years , 12, 35.Both the Probability and the Distance methods have limitations, and many improvements could be proposed.The Probability method is the most beneficial in decreasing the global distributional bias, since it considers many variables at once, but may increase bias in specific characteristics. This happens when the amount of distortion in one characteristic is much higher than in another. The model accepts a trade-off (more distortion) on the less biased variable in order to optimize the global fit. In our case, the algorithm slightly increased the bias regarding hospital size to compensate for the highly distorted geographical distribution . This phenomenon may influence the outcome of the study itself if the bias is increased for strongly predictive variables. We indeed observed a higher HAI prevalence in the Probability sample, due to the greater quota of larger hospitals which were included by the algorithm to improve the regional representativeness . A possible solution could be to reweight the contribution of the variables to hospital selection according to their statistical relevance with the outcome. The QS has a strong impact on the Probability method, but we showed that hospital selection is still largely driven by the country distribution of hospitals; alternatively, different weights could be attributed to the QS by exponentiating it after the rescaling , especially if these have many possible values. This bias is compensated by a random search for solutions that improves the global fit , but the candidate hospitals are still evaluated using the same hierarchy.We provide the R code for both methods (Supplementary Material SA general limitation of subsampling for bias correction is that it strongly impacts the sample size and, therefore, the power of studies. Only if the initial convenience sample is large, enough margin exists to compensate for the bias by subsampling. An alternative could be first to oversample the original data, increasing the size artificially, and then to produce a subsample of the original size but sub/over-sampled in order to reduce distortions.Further validation of the sub-sampling methodologies may be pursued by taking advantage of countries with sample coverage near 100% in the European PPS sample: various distortive convenience sampling strategies may be simulated and to test how efficient are the subsampling methods in retrieving the real population prevalence.The presented methods have been demonstrated in the context of Healthcare Associated Infection prevalence estimation in the presence of a convenience sample of acute care hospitals, but their applicability is indeed generalizable to a number of contexts and problem, wherever bias in the sample representativity and data quality issue are possibly present. For example, sample representativity isssues are common in designs that requires the opt-in of the participant units, like survey-based , cohort Our analysis suggests that non-probabilistic sampling strategies might produce significant distortions in the distribution of the characteristics of a sample. This may introduce biases in the estimates which are challenging to evaluate and adjust for at the analysis stage. Therefore, we created a set of algorithms aimed at reducing this bias by producing a subsample theoretically more similar to the target population. Furthermore, we developed a quantitative score to evaluate the data quality of observational units participating in a study; this score may help in pre-analysis error individuation and correction and to improve research protocols but can also utilized to remove low quality data points if no relevant influence on the population parameters of impact is observed.Additional file 1. Rules for computing the quality score in the Italian PPS on HAI and AMU.Additional file 2. Code implementation of the subsampling algorithms.


Short-term traffic prediction under corrupted or missing data for large-scale transportation networks has become an important and challenging topic in recent decades. Since the critical roads have predictive power on their adjacent roads, this paper proposes a novel hybrid short-term traffic state prediction method based on critical road selection optimization. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method is introduced, which is considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the case of considering critical road sections. Real-time traffic state prediction plays a vital role in traffic management and public service. By predicting the evolution of traffic timely and accurately, governments and travelers could react to the traffic congestion ahead of time. For instance, intelligent transportation systems, advanced traffic management systems, and traveler information systems depend on real-time traffic state prediction.In the past decades, there have been numerous studies on this topic . These sData-driven methods have achieved promising results due to more potential in processing complex nonlinear problems . These mHowever, most of the deep learning approaches are constructed based on the entire dataset. The attributes of datasets influence the prediction performance. In other words, the deep learning approaches have highlighted the data quality in short-term traffic state prediction . Due to Recently, researchers find that some critical roads significantly affect the traffic states of their adjacent roads at a specific road network , 8. ThisThere have been numerous studies on this topic, but only a tiny percentage of them have paid attention to prediction based on critical road selection optimization. Therefore, if we extract the traffic state of the critical road sections with the most dominant predictive power, we could characterize the spatiotemporal features of traffic flow and predict the future traffic state of the overall network.In this paper, we propose a novel hybrid short-term traffic state prediction method based on critical road selection optimization. Different scores were given to data collected on different road segments. The higher the criticality of the road segment, the higher the score of the data. The critical road selection problem is abstracted as a multiobjective optimization problem, maximizing the sensing coverage and data scores. In other words, our objective is to select the most suitable critical roads to maximize the quality of the service (QoS) with the limited consumption of network resources.Then, a novel critical road selection method is proposed, considering the topological structure and the mobility of the urban road network. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Finally, to demonstrate the effectiveness of the proposed method, the numerical experiments using the traffic states depicted from GPS trajectory data in Beijing. In addition, other traditional machine-learning models are compared to demonstrate the advantage of the proposed method. Practical experiment results showed that the proposed method could precisely predict future traffic network states.The rest of our paper is organized as follows. Over the past decades, considerable short-term traffic forecast models have emerged to handle the prediction of future traffic states ranging from a few seconds to few hours. These approaches could be generally categorized into mathematical approaches, statistical approaches, and data-driven approaches.Mathematical approaches focus on predetermining the model structure by theoretical assumption. The evolution of the traffic state could be simulated by the theoretical mathematical models . BecauseStatistical approaches usually rely on statistical assumptions. The autoregressive integrated moving average (ARIMA) and its variants are the most common mathematical approaches. Hamed et al.  proposedIn addition, the Markov chain, Kalman filter (KF), KF-based approaches, and other approaches also have important applications in short-term traffic prediction –24. HoweDifferent from mathematical approaches and statistical approaches, data-driven approaches rely on a sufficient mass of traffic data. In recent years, due to abundant data attached to extensive traffic sensors and advanced big data technology, data-driven approaches have developed rapidly. Numerous data-driven approaches were put forward for the short time traffic state prediction, such as SVM –28, neurAmong these data-driven approaches, deep learning methods have become extremely popular and successful because of their powerful ability to process nonlinear high-dimensional problems. Huang et al. employed a deep belief network (DBN) with multitask learning for traffic flow prediction . Lv et aIn summary, among all these traffic state prediction approaches, deep learning methods stand out as the most potent alternative. However, few prediction methods could achieve satisfactory accuracy because of the missing data problem in reality. For the critical roads that have a significant effect on the traffic states of their respectively adjacent roads at a specific road network, we could extract the traffic state of the critical road sections and predict the future traffic state of the overall network.This research focuses on utilizing data from critical road sections to predict future traffic conditions of the overall urban transportation network. In this section, the utility function of the QoS for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Specifically, the critical road sections are selected by an innovative critical road selection method. Finally, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network.In the practical urban road network, road sections are connected at intersections and eventually form a complex network. When performing sensing tasks in urban road network areas, the topological structure characteristics of the road network need to be considered. The results of numerous studies have shown that the topology of urban road networks is complex and diverse. The failure of connectivity in some sections of the network can impact the traffic operation in the whole network. However, it is inevitable that road connectivity in the network will sometimes be interrupted due to bad weather, technical failures, or serious accidents. This will lead to local traffic congestion and paralysis in the network, which will have harmful economic and social impacts. It means that the higher importance of road sections in the network under different spatial and temporal conditions indicates the more severe damage to the integrity of the road network connectivity brought by their failure. In order to avoid serious impacts on road network connectivity and vehicle movements, priority should be given to the use of high, critically important road sections for future traffic state prediction.In order to study the structural properties of real road networks, it is necessary to reasonably abstract the road networks into topological structure diagrams, consisting of points and lines by suitable methods. The primal approach and the dual approach are two methods to abstract the road network into a complex network model. The primal approach uses the roads in the road network as edges in the abstract network and the intersections as nodes in the abstract network. The dual approach models the intersections as edges in the network and the roads in the urban road network as nodes, thus showing the interconnections between roads in a highly abstract way. The primal approach is intuitive and simple and can retain the layout characteristics of the road network. The dual approach ignores some geographic significance of network entities, such as the geographic location of roads, length, and width, and therefore, it is more suitable for analyzing abstract network structures. In this paper, we pay more attention to the characteristics of road connectivity, so we use the primal approach to model the road network.The QoS is the standard for measuring the performance of the critical roads in a prediction task. The greater the value of QoS, the better the prediction performance of the critical roads. Here, we propose a utility function to calculate the QoS for the critical roads.C, we divide the road network into small grids, and the grid is used as the basic measuring element for coverage calculation. As shown in The coverage of the selected roads is mainly concerned with people when predicting the traffic state of the whole road network. So, we choose coverage as our first metric. Coverage indicates the coverage of the selected vehicle to the entire road network. For better calculating the coverage gj as the single grid in the urban road network. Let f(gj) represent the coverage state of gj, and f(gj)=1 indicates that grid j is selected during the predicting task time; otherwise, f(gj)=0. In this paper, we tried to predict the whole network traffic state. If gj belongs to road section l and road section l has φ grids, then the road factors could be calculated as ∂(gj)=1/φ. The following equations could calculate the coverage of the selected grids: Define On the other hand, we introduce the concept of data score to represent the criticality level of the roads. Specially, we give a higher score to the data collected from the critical roads than the data collected from the ordinary roads. Here, we use the correlations among road sections on both space and time to calculate the data score.k. Suppose there exists a network, which can be represented as G=, where V′ is the set of nodes in the network and E is the set of edges in the network, representing the connections between nodes in the network. The network G= has |V′|=N nodes and |E|=M edges. Then, this network can be represented by the adjacency matrix AM×N. If nodes i and j are connected directly or indirectly, we call these two nodes “kth-order neighbors,” and their adjacency relationship could be expressed as equation Aijk=xit be traffic speed in the road section i at time t, where xit=. Then, the integrated speed values of the adjacent road sections RXit could be computed by equation (Di(s) between road section i and its adjacent road sections could be calculated by equation  (s > 0) is the time lag between the speeds of road sections i and its adjacent road sections; xit in road sections i during the time duration of T. it. Based on the previous analysis, mathematical expressions of data score D are defined as follows: And, to comprehensively consider the spatial correlation of the road network with the temporal correlation, the spatial weights matrix is introduced as a spatial indicator to improve the initial correlation distance. Let equation . Then, tequation :(4)RXit=U, is defined asα ∈  is a parameter for tuning the weight of data coverage and data score. To make α more sensitive in equation  represents whether grid j is selected, with f(gj)=1 indicating that grid j has been selected; otherwise, the value of f(gj) is 0. F is the set of critical roads, and C is the coverage. Di(s) is the data score between road section i and its adjacent road sections. μ is the threshold for the selection.The goal of the critical road selection model is to maximize the QoS of the urban traffic prediction system with limited critical roads, so the mathematical expression of the critical roads selection problem can be defined as T. An improved greedy algorithm is proposed to solve the critical road selection problems. A greedy algorithm means that we always make the best choice for the current situation when solving an optimization problem. In other words, such an algorithm does not consider the overall situation but rather considers only the local situation. The pseudocode of the proposed algorithm is shown in Based on the above analysis, a critical road selection model for traffic state prediction is constructed. In this paper, due to the spatiotemporal state changes of the road network, the selected roads need to be updated at intervals In this paper, a spatiotemporal recurrent convolutional network is proposed for the prediction (STRCN). The proposed STRCN inherits the advantages of deep convolutional neural networks (DCNN) and long short-term memory (LSTM) neural networks. The spatial dependencies of network-wide traffic can be captured by CNN, and the temporal dynamics can be learned by LSTM.CNN has been successfully applied to traffic prediction for its great potential in extracting features using multiple layers. A typical CNN mainly comprises multiple convolution layers and pooling layers. The former contributes to mine spatial dependencies of road sections since every layer retrieves a distinct feature using different filters. In comparison, the latter assists in reducing the number of parameters required for training CNN under the premise of ensuring prediction accuracy. Given that the input for CNN could be intuitively regarded as an image with each pixel value associating one kind of traffic state during a certain time, 2D CNN is naturally utilized to abstract spatial features between road sections. Vt+a={vit+a}i=1m, where a is the prediction horizon and m is the number of road sections. The input of the CNN is the historical traffic speed of critical road sections {Ut−n, Ut−1, Ut}, where Ut−n={vit−n}i=1Pα represents the traffic state of critical road sections at time (t−n), n is the look back step, and Pα is the number of critical road sections. Then, the spatial features of input are captured by convolutional and pooling layers. Let Orl be the output of lth convolutional and pooling layers with r filters and the weights and bias of lth layers be . Then, the Orl could be calculated by the following equation:Orl−1 means the output of the previous layer and Or1 is exactly the input layer. f is a nonlinear activation function, and pool denotes the pooling procedure.Suppose that we need to predict the future traffic speed of the network Intuitively, traffic states at each moment have a strict sequential relationship in time dimension rather than isolated from each other, which is especially suitable for RNN to capture the temporal evolution of traffic flow. However, it is difficult for traditional RNN to capture temporal dependency if two time intervals are remote. Then, LSTM, one of the specific forms of RNNs, is proposed to tackle these issues by adding memory cells in hidden layers. As shown in Vt={xit}i=1R as its input to predict the future traffic states Ht={hit}i=1q, namely, its output, where q is the number of hidden units of the output layer. For a memory cell, the input states are Gt−1 while the output is Gt. Meanwhile, the states of input, forget, and output gates are It, Ft, and Ot, respectively. The temporal features could be iteratively calculated by the following equations:W and bias vectors b are constructed to connect input layer, output layer, and the memory cell, ⊙ denotes the scalar product of two vectors, and σ(·) represents the standard logistics sigmoid function defined as follows: In our model, next to CNN, the LSTM naturally takes the output of CNN Wy and by are weight and bias related to the hidden layer. Conclusively, the model is trained from end to end, and the values of Yt+1 are prediction results, which are the output of the entire mode. Several hyperparameters within the model will be set and elaborated in the experiment section. Additionally, it is significant to note that the input size will alter as the number of critical road sections changes due to different extracting rate α, and hence several hyperparameters will change too.Integrated with the advantages of CNN and LSTM, the STRCNs is utilized to predict future traffic states by sufficiently exploiting the spatiotemporal characteristics of the data. Eventually, a fully connected layer is employed to predict the future speed by taking the output of LSTM as input. The future speed could be calculated by the following equation:2 areas.In this section, a case study is conducted to evaluate the performance of the proposed critical road selection optimization model and the traffic prediction method. The main urban road of a subtransportation network of Beijing near West Second Ring Road is selected as the research objective, as shown in Data collected by taxis equipped with GPS devices from June 1st, 2015, to August 31st, 2015 (92 days) is utilized for training the proposed model and predicting the future traffic speed of the network. The updating frequency of data is 2 minutes, and a time period ranging from 6 : 00 : 00 to 23 : 00 : 00 is concerned for high travel demand is repeatedly observed. Accounting for the traffic state varies every time interval, we could observe 511 traffic states per day.μ .The proposed QoS-based critical road selection method and other two road selection methods (random method and coverage-based method) were used to select the road sections for prediction in the road network. In a random method, the road is selected randomly during the prediction process. In the coverage-based method, the road is selected based on the coverage, which means that roads with higher coverage are preferred for selection. The three critical road selection methods are applied under different roads extracting rate μ=0.50 means that we will select 139 roads as critical road sections and subsequently use them to predict the traffic states of 278 roads.Eventually, the correspondence of extracting rate and the number of critical road sections are listed in ith ground-truth value and yi is the ith predicted value. The value of nμ denotes the number of critical road sections at extracting rate μ and Nμ is the total number of traffic states.The root means squared error (RMSE) and root mean squared error proportional (RMSEP) are employed to evaluate the performance of all the models, which could be calculated as in the following equations:μ are listed in In order to evaluate the performance of the proposed critical road selection optimization model, we train and test the STRCN model using different road selection methods. The RMSEs and RMSEPs in the context of different extracting rates It could be found that in the range of 0.8 to 1.0, the performance of the STRCN model using different road selection methods is almost the same. The reason is that the finally selected road sections by different methods have little difference under the condition of high extracting rate. However, when the extracting rate is between 0.5 and 0.8, the performance of the QoS-based selection method is a little superior to the overall prediction model. In general, the decrease of accuracy is reasonable and within the acceptable limits, which demonstrates the validation and generalization of the approach and the fact that some road sections indeed have less contribution towards prediction.Additionally, when the extracting rate comes to 0.5, the predictive performance gradually tends to be unstable, probably because too many roads are omitted. Particularly when the extracting rate is between 0.0 and 0.2, the performance is basically the same. That is because no matter what method is used, there are too many missing road sections to predict. μ where the value remains the same as those mentioned above.As we introduced before, corrupted or missing data generally exist on account of the monitoring equipment failure, extreme weather, data transmission error etc., which weakens the effectiveness of the prediction model or even disables the model. To test the performance of our model under random structural missing data, we stochastically extract a part of road sections of the rate of Four popular deep learning-based algorithms are selected for comparison, including ANN, CNN, LSTM, and SAE. ANN adopts a plain and shallow structure to process multidimension and nonlinear problems. The parameters of the SAE are set according to , which aStructural missing data usually has a massive negative effect on short-term traffic state prediction. In this paper, a novel hybrid short-term traffic state prediction method based on critical road selection optimization is proposed. First, the utility function of the quality of service (QoS) for the critical roads in a large-scale road network is proposed based on the coverage and the data score. Then, the critical road selection optimization model in the transportation networks is presented by selecting an appropriate set of critical roads with the maximum proportion of the total calculation resources to maximize the utility value of the QoS. Also, an innovative critical road selection method, which is considering the topological structure and the mobility of the urban road network, is introduced. Subsequently, the traffic speed of the critical roads is regarded as the input of the convolutional long short-term memory neural network to predict the future traffic states of the entire network. Experiment results on the Beijing traffic network indicate that the proposed method outperforms prevailing DL approaches in the cases of considering critical road sections.However, even the case study showed that the proposed method could significantly improve the QoS of the traffic prediction, there is still a long way to go from practical application for the method. For future studies, the inherent attributes of the road should be taken into account when calculating the QoS for the road network. Besides, the traffic accident, temperature, weather, and other external factors affect the traffic prediction accuracy. All these will be left for our future research.


This study describes the feasibility of using the Canadian Occupational Performance Measure (COPM) as a multidisciplinary outcome measure for pediatric telerehabilitation (TR). The COPM was administered at monthly time points over four months. A follow-up survey was conducted with the therapists to assess clinical utility of the COPM. Seventy-three percent of the children seen in TR > one month had at least two administrations of the COPM. Eighty percent of therapists agreed or strongly agreed that the COPM was easy to use in a reasonable amount of time, helped identify functional goals, could be used with various children with varied diagnoses, and measured functional change. In 37 children, the median clinical change in performance and satisfaction was two points or greater on the COPM over the episode of TR. The COPM is a feasible measure perceived positively by pediatric therapists for TR use. Children across the world receive pediatric rehabilitation services secondary to functional impairments impacting quality of life. The Coronavirus (COVID-19) pandemic brought imminent challenges to the rehabilitation community providing occupational therapy (OT), physical therapy (PT), and speech-language pathology (SLP) services to children, adolescents, and young adults. To comply with stay-at-home orders to reduce the spread of the disease and to reduce use of personal protective equipment, many institutions responded with the long-awaited offering of telerehabilitation (TR) to provide these services safely and responsibly. While many terms are used in the field of telehealth, the American Telemedicine Association defines telerehabilitation as “the delivery of rehabilitation and habilitation services via a variety of communication technologies….that include evaluation, assessment, monitoring, prevention, intervention, supervision, education, consultation and coaching” . In the While the pandemic accelerated the adoption of TR secondary to necessity and increased reimbursement for the services, researchers have investigated the benefits over the last decade at a minimum. A systematic review completed in 2008 included 28 articles primarily encompassing adult rehabilitation and found positive clinical outcomes utilizing TR interventions that were similar or better than comparison interventions . More reVery few outcome measures are valid for use by multiple disciplines. The Canadian Occupational Performance Measure (COPM) is accepted for use by occupational therapists and allied health professionals . It has Speech and language therapists have used the tool as well . The COPAs pediatric TR grows, there is a need to identify appropriate outcome measures that can be used to assess the quality of care that is being provided by multiple disciplines. Pediatric rehabilitative care is grounded in the model of family-centered care . The COPOur study took place in a pediatric outpatient hospital-based system during the spring and summer of 2020 during the COVID-19 pandemic. The process for the quality improvement (QI) project followed “plan-do-study-act” cycles  Figure  and SQUIThe COPM was chosen as it has established psychometric properties for validity, test-retest reliability, and responsiveness to change in pediatric populations . It was Therapists from multiple disciplines  were provided education on using the COPM as an outcome measure and invited to volunteer for the project. Twelve out of a total of 54 therapists providing TR volunteered to participate in the QI project with representation from all three disciplines . Their pThe investigators designed a survey with a focus on demographics of the therapists, perceived ease of use, applicability to the populations served, and relationship to functional change. It was administered to the participating therapists within one week following completion of the study. It used a five-point Likert scale with defined criteria for each point on the scale.Data were gathered from the medical record, de-identified, and analyzed at the end of the 4-month time frame. The feasibility of implementation was assessed by analyzing the frequency and consistency of use of the COPM over time with children scheduled for TR with the volunteer therapists. The clinical utility of the COPM for TR was assessed with content analysis of the therapist survey. Trends in the data were studied in a secondary analysis, which included the children's demographics, standard deviation, and median change on the COPM for all children with two or more data points.The study's scope and aims were evaluated by the institutional review board at our institution and determined to qualify for an exemption.Twenty-two percent of the therapists performing TR at the time of the study volunteered to participate . Eighty-Ten of the twelve therapists (83%) in the project responded to the survey, with representation from all three disciplines. Eighty percent or more of the respondents agreed or strongly agreed that the COPM was easy to use in a reasonable amount of time, helped identify functional goals, could be used with a variety of children with varied diagnoses, and was able to measure functional change . ReasonsChildren were seen from a wide range of ages  and multDue to the rapid growth of telemedicine, and in this case, TR via videoconferencing, it is imperative to quantify the quality of rehabilitation services. Having a patient-reported outcome that can be utilized across disciplines in a time-efficient and purposeful manner without physical measurement allows for efficient analysis of service quality. This quality improvement initiative demonstrated adequate feasibility of the use of the COPM across disciplines in pediatric TR. Two observations in this initiative point towards the COPM as a promising outcome in TR: (1) the positive response of therapists regarding its utility across a multidisciplinary team, and (2) the ability to complete the assessment at least two times in over 70% of the appropriate patients during the initial 6 months of a TR program. The initial findings support exploring the use of the COPM on a broader scale in pediatric TR.Therapists from all three rehabilitation disciplines reported the positive clinical utility of the COPM in this small sample. This is consistent with the recommendation of the COPM for use in multidisciplinary pediatric practice, such as early intervention services  and chilThe therapist survey results demonstrated concern regarding the caregiver or child's ability to distinguish between the participation and the satisfaction rating for goals. Other research has also highlighted this concern when using the COPM . A possiThere were multiple limitations to this QI initiative when considering the next steps of clinical improvement or generalizability into a multidisciplinary context. This QI initiative did not address parent/caregiver satisfaction of the use of the COPM; however, previous literature in small samples demonstrates that caregivers value goal setting within the realm of pediatric rehabilitation  and alsoth edition) and the Speech Intelligibility Rating for Pediatric Language Assessment (nd edition) for motor performance (With the increase in TR services provided during the COVID-19 pandemic and reported caregiver satisfaction of pediatric TR , it is esessment  or the Mformance ; however