In this work, we decided to apply a hierarchical weighted decision, proposed and used in other research fields, for the recognition of gait phases. The developed and validated novel distributed classifier is based on hierarchical weighted decision from outputs of scalar Hidden Markov Models (HMM) applied to angular velocities of foot, shank, and thigh. The angular velocities of ten healthy subjects were acquired via three uni-axial gyroscopes embedded in inertial measurement units (IMUs) during one walking task, repeated three times, on a treadmill. After validating the novel distributed classifier and scalar and vectorial classifiers-already proposed in the literature, with a cross-validation, classifiers were compared for sensitivity, specificity, and computational load for all combinations of the three targeted anatomical segments. Moreover, the performance of the novel distributed classifier in the estimation of gait variability in terms of mean time and coefficient of variation was evaluated. The highest values of specificity and sensitivity (>0.98) for the three classifiers examined here were obtained when the angular velocity of the foot was processed. Distributed and vectorial classifiers reached acceptable values (>0.95) when the angular velocity of shank and thigh were analyzed. Distributed and scalar classifiers showed values of computational load about 100 times lower than the one obtained with the vectorial classifier. In addition, distributed classifiers showed an excellent reliability for the evaluation of mean time and a good/excellent reliability for the coefficient of variation. In conclusion, due to the better performance and the small value of computational load, the here proposed novel distributed classifier can be implemented in the real-time application of gait phases recognition, such as to evaluate gait variability in patients or to control active orthoses for the recovery of mobility of lower limb joints.

Markerless human motion capture could be a potential tool for studying body biomechanics. Despite the extensive previous studies, the design of an accurate markerless system able to perform a quantitative clinical gait analysis is still an open challenge. Our purpose was to characterize the performance of a low-cost markerless system, consisting of the open-source library OpenPose, two webcams and a linear triangulation algorithm. The system was validated in terms of 3D kinematic gait analysis, by comparison with inertial sensors. Two synchronized videos of six healthy subjects were recorded in three webcam configurations, in walking and running sessions on a treadmill. Sagittal joint angles were compared between the two systems, to evaluate the kinematic performance of the markerless system. Results showed that the angular paths, provided by OpenPose, were close to the ones computed by inertial sensors in all camera positions, but OpenPose inaccuracy affected the computation of joint angles parameters producing absolute errors up to 14.0°± 1.8°. The observed differences depended on OpenPose ability in the recognition of joints centers and on camera configurations. In the optimal condition (joint visible from both cameras during the whole trial) our system accuracy resulted equal to 1.6 °± 0.3 °. This system could be useful in those applications where a real-time body tracking is required, i.e. video games or virtual rehabilitative trials. When a quantitative analysis is needed, the low level of accuracy may be overcome, by both increasing the cameras' number and implementing an optimization triangulation algorithm.

This paper presents the modular design and control of a novel compliant lower limbmulti-joint exoskeleton for the rehabilitation of ankle kneemobility and locomotion of pediatric patients with neurological diseases, such as Cerebral Palsy (CP). The device consists of an untethered powered knee-ankle-foot orthosis (KAFO), addressed as WAKE-up (Wearable Ankle Knee Exoskeleton), characterized by a position control and capable of operating synchronously and synergistically with the human musculoskeletal system. The WAKE-up mechanical system, control architecture and feature extraction are described. Two test benches were used to mechanically characterize the device. The full system showed a maximum value of hysteresis equal to 8.8% and a maximum torque of 5.6 N m/rad. A pre-clinical use was performed, without body weight support, by four typically developing children and three children with CP. The aims were twofold: 1) to test the structure under weight-bearing conditions and 2) to ascertain its ability to provide appropriate assistance to the ankle and the knee during overground walking in a real environment. Results confirm the effectiveness of the WAKE-up design in providing torque assistance in accordance to the volitionalmovements especially in the recovery of correct foot landing at the start of the gait cycle.

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°.

Magnetic inertial measurement unit systems (MIMU) offer the potential to perform joint kinematics evaluation as an alternative to optoelectronic systems (OS). Several studies have reported the effect of indoor magnetic field disturbances on the MIMU's heading output, even though the overall effect on the evaluation of lower limb joint kinematics is not yet fully explored. The aim of the study is to assess the influence of indoor magnetic field distortion on gait analysis trials conducted with a commercial MIMU system. A healthy adult performed gait analysis sessions both indoors and outdoors. Data collected indoors were post-processed with and without a heading correction methodology performed with OS at the start of the gait trial. The performance of the MIMU system is characterized in terms of indices, based on the mean value of lower limb joint angles and the associated ROM, quantifying the system repeatability. We find that the effects of magnetic field distortion, such as the one we experienced in our lab, were limited to the transverse plane of each joint and to the frontal plane of the ankle. Sagittal plane values, instead, showed sufficient repeatability moving from outdoors to indoors. Our findings provide indications to clinicians on MIMU performance in the measurement of lower limb kinematics.