Automatic detection of faults in race walking. A comparative analysis of machine-learning algorithms fed with inertial sensor data

The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition

On the reliability and repeatability of surface electromyography factorization by muscle synergies in daily life activities

Muscle synergy theory is a new appealing approach for different research fields. This study is aimed at evaluating the robustness of EMG reconstruction via muscle synergies and the repeatability of muscle synergy parameters as potential neurophysiological indices. Eight healthy subjects performed walking, stepping, running, and ascending and descending stairs' trials for five repetitions in three sessions. Twelve muscles of the dominant leg were analyzed. The “nonnegative matrix factorization” and “variability account for” were used to extract muscle synergies and to assess EMG goodness reconstruction, respectively. Intraclass correlation was used to quantify methodology reliability. Cosine similarity and coefficient of determination assessed the repeatability of the muscle synergy vectors and the temporal activity patterns, respectively. A 4-synergy model was selected for EMG signal factorization. Intraclass correlation was excellent for the overall reconstruction, while it ranged from fair to excellent for single muscles. The EMG reconstruction was found repeatable across sessions and subjects. Considering the selection of neurophysiological indices, the number of synergies was not repeatable neither within nor between subjects. Conversely, the cosine similarity and coefficient of determination values allow considering the muscle synergy vectors and the temporal activity patterns as potential neurophysiological indices due to their similarity both within and between subjects. More specifically, some synergies in the 4-synergy model reveal themselves as more repeatable than others, suggesting focusing on them when seeking at the neurophysiological index identification

Quantifying age-related differences of ankle mechanical properties using a robotic device

A deep analysis of ankle mechanical properties is a fundamental step in the design of an exoskeleton, especially if it is to be suitable for both adults and children. This study aims at assessing age-related differences of ankle properties using pediAnklebot. To achieve this aim, we enrolled 16 young adults and 10 children in an experimental protocol that consisted of the evaluation of ankle mechanical impedance and kinematic performance. Ankle impedance was measured by imposing stochastic torque perturbations in dorsi-plantarflexion and inversion-eversion directions. Kinematic performance was assessed by asking participants to perform a goal-directed task. Magnitude and anisotropy of impedance were computed using a multiple-input multiple-output system. Kinematic performance was quantified by computing indices of accuracy, smoothness, and timing. Adults showed greater magnitude of ankle impedance in both directions and for all frequencies, while the anisotropy was higher in children. By analyzing kinematics, children performed movements with lower accuracy and higher smoothness, while no differences were found for the duration of the movement. In addition, adults showed a greater ability to stop the movement when hitting the target. These findings can be useful to a proper development of robotic devices, as well as for implementation of specific training programs

Between-day repeatability of lower limb EMG measurement during running and walking

There are minimal data describing the between-day repeatability of EMG measurements during running. Furthermore, there are no data characterising the repeatability of surface EMG measurement from the adductor muscles, during running or walking. The purpose of this study was to report on the consistency of EMG measurement for both running and walking across a comprehensive set of lower limb muscles, including adductor magnus, longus and gracilis. Data were collected from 12 lower limb muscles during overground running and walking on two separate days. The coefficient of multiple correlation (CMC) was used to quantify waveform similarity across the two sessions for signals normalised to either maximal voluntary isometric contraction (MVIC) or mean/peak signal magnitude. For running, the data showed good or excellent repeatability (CMC=0.87-0.96) for all muscles apart from gracilis and biceps femoris using the MVIC method. Similar levels of repeatability were observed for walking. Importantly, using the peak/mean method as an alternative to the MVIC method, resulted in only marginal improvements in repeatability. The proposed protocol facilitated the collection of repeatable EMG data during running and walking and therefore could be used in future studies investigating muscle patterns during gait

A markerless system for gait analysis based on OpenPose library

The paper reports the performance of a low-cost markerless system for 3D human motion detection and tracking, consisting of the open-source library OpenPose, two webcams and a linear triangulation algorithm. OpenPose is able to identify anatomical landmarks with a commercial webcam, using Convolutional Neural Networks trained on data obtained from monocular images. When images from at least two different points of view are processed by OpenPose, 3D kinematic and spatiotemporal data of human gait can be also computed and assessed. Despite its potential, the accuracy of such a system in the estimation of kinematic parameters of human gait is currently unknown. With the aim to estimate OpenPose accuracy in 3D lower limb joint angle measurement during gait, two synchronized videos of a healthy subject were acquired, with two webcams, in a walking session on a treadmill at comfortable speed. 2-dimensional joint centers coordinates were assessed by OpenPose, and computed in 3D by triangulation algorithm. The resulting angular kinematics was, then, compared with inertial sensors outputs. Results showed that the system was generally able to track lower limbs motion, producing angular traces representative of normal gait similar to the ones computed by IMUs. However, OpenPose approach showed inaccuracy, mostly in the computation of maxima and minima joint angles, reaching error values up to 9.9°

EMG factorization during walking: Does digital filtering influence the accuracy in the evaluation of the muscle synergy number?

This paper aims at verifying if the number of muscle synergies is influenced by the filtering process. Firstly, EMG activity of 12 muscles of the lower limb of a healthy subject was recorded during walking trial. Then, the Wavelet-Independent Component Analysis was used for the separation of the EMG, considered as reference signal, and the noise source from the raw experimental data. To test the effect of different order and cut-off frequencies of the high-pass and low-pass filtering on the numbers of muscle synergies, a Monte Carlo simulation was performed on the EMG signals. In particular, a dataset of 105 raw EMG signals were generated by adding different noise signals to the reference signal. The reference dataset was pre-processed with different combination of order and cut-off frequency of low-pass filter for the envelope extraction. The simulated dataset, instead, was pre-processed with different combinations of high and low pass filtering. A non-negative matrix factorization was performed to extract the muscle synergies on both the reference dataset and the simulated ones. For each filtering combination, the percentage of the Number of Synergies NoS within each simulated dataset was computed and compared with the NoS obtained from the reference dataset. The reference dataset produced a number of synergies equal to 4 for all the combinations of the tested low-pass filters, proving that, for the EMG signal without artifact, the number of muscle synergies is robust with respect to different low-pass filtering parameters. Conversely, the simulated dataset generated different numbers of synergies in different filtering conditions, ranging from 3 to 5. However, some particular combinations of filtering parameters produced no variation on Nos. Our results demonstrate the importance of choosing the most appropriate filtering parameters, as it resulted that low order and cut-off frequencies lead to an underestimation of the number of synergies, while high orders and cut-off frequencies cause an overestimation of the number of muscle synergies. In this vein, the present paper offers guidelines for the choice of the best filtering condition in muscle synergy evaluation

Real-time gait detection based on Hidden Markov Model: is it possible to avoid training procedure?

In this paper we present and validate a methodology to avoid the training procedure of a classifier based on an Hidden Markov Model (HMM) for a real-time gait recognition of two or four phases, implemented to control pediatric active orthoses of lower limb. The new methodology consists in the identification of a set of standardized parameters, obtained by a data set of angular velocities of healthy subjects age-matched. Sagittal angular velocities of lower limbs of ten typically developed children (TD) and ten children with hemiplegia (HC) were acquired by means of the tri-axial gyroscope embedded into Magnetic Inertial Measurement Units (MIMU). The actual sequence of gait phases was captured through a set of four foot switches. The experimental protocol consists in two walking tasks on a treadmill set at 1.0 and 1.5 km/h. We used the Goodness (G) as parameter, computed from Receiver Operating Characteristic (ROC) space, to compare the results obtained by the new methodology with the ones obtained by the subject-specific training of HMM via the Baum-Welch Algorithm. Paired-sample t-tests have shown no significant statistically differences between the two procedures when the gait phase detection was performed with the gyroscopes placed on the foot. Conversely, significant differences were found in data gathered by means of gyroscopes placed on shank. Actually, data relative to both groups presented G values in the range of good/optimum classifier (i.e. G <= 0.3), with better performance for the two-phase classifier model. In conclusion, the novel methodology here proposed guarantees the possibility to omit the off-line subject-specific training procedure for gait phase detection and it can be easily implemented in the control algorithm of active orthoses

Can the measurements of leg stability during jump landing predict and monitor anterior cruciate ligament injury? A case report of basketball player

Basketball is a contact sport game that includes important stresses of the lower limb joints. It follows an increased risk of developing acute traumatic pathologies, among which the lesion of the anterior cruciate ligament of the knee (ACL). The study aims at investigating the presence of ACL injury risk factors detectable with a wearable inertial system in a national competitive basketball player subjected to prospective collegial surveys, as well as indices for monitoring the return to play of the athlete. The injured participant suffered the ACL rupture during an international basketball match, which took place a month after the first evaluation session. The athlete underwent ACL reconstruction followed by a 6-months rehabilitation period. The evaluation of jump landing task was performed before and after the knee injury with the OPTOGait and GYKO sensors. The results showed significant differences in parameters of the injured lower limb compared to the contralateral limb in the pre-surgery evaluation. In addition, several leg stability parameters, such as ellipse area and path length in all directions, can be considered as useful indices to decide the return to competition of the athlete. The outcomes open the possibility to perform sport-specific quantitative analysis to obtain indices useful to assess the risk of ACL injury and to monitor the progress during the rehabilitation program