Stefania Sozzi

Exoskeletons are a fast-growing technology that enables multiple use-cases in clinical scenarios. They can be useful tools for the rehabilitation of patients with motor dysfunctions caused by neurological conditions, aging or trauma. Assistive exoskeletons modulate the torque exerted by the electrical motors moving their joints to allow the patients wearing them to achieve an intended movement, such as gait, correctly. Their effectiveness, therefore, requires accurate online control of such torques to complement those generated by the patient. Hereby we explored Deep Learning (DL) models to generate an online prediction of the gait phase, i.e., stance or swing, during assisted walking with a lower-limb exoskeleton based on surface electromyography (sEMG) data. We leveraged the lead of muscular activation with respect to the movement of the limbs to adjust the labeling based on joints kinematics. The cross-subject design allowed to generalize over subjects not considered for training A hyperparameter optimization algorithm was also implemented to further explore the capabilities of DL models of a reduced size. We simulated a use case scenario to assess whether online implementation of the proposed technique is feasible. We also proposed a new metric called trade-of score (TOS) for evaluating the cost-performance compromise of the optimized models which lead to identifying a DL model capable of classifying gait phases with an accuracy of about 95% while significantly reducing the number of parameters compared to the full architecture. Its mean computational time of less than 10 ms offers the opportunity for accurate, online exoskeleton control based on sEMG data.

Objectives: We assessed the difference between quiet stance and gait in the spatial distribution and intensity of foot plantar pressures and whether it is possible to estimate the distribution during gait from data obtained during stance. Methods: A total of 60 healthy subjects with a mean age of 31.0 ± 9.4 years performed two trials for quiet stance and four trials for gait on a baropodometric walkway with their eyes open. Foot plantar pressures were recorded from 10 areas of the foot sole. Results: During quiet stance, the highest plantar pressure occurred at metatarsal heads (M2 to M4) and the medial (MH) and lateral halves of the heel (LH). During gait, the profile of plantar pressure values was like that during stance, but significantly higher. The differences concentrated at the big toe (T1), M2 to M4, MH, and LH, whilst toes (T2,3,4,5) and midfoot (MF) showed the smallest difference. A significant positive correlation was found between the corresponding areas of foot pressure during gait and stance. Conclusions: During quiet stance and gait, the overall profile of plantar pressure distribution was similar. During quiet stance, the subjects loaded more on the heels, in keeping with the known position of the center of pressure just in front of the ankles. During gait, higher pressures on the metatarsal areas are related to the forward propulsion of the center of mass. The correlation between the corresponding areas of foot pressure during gait and stance suggests that the pressure distribution during gait can partly be estimated from that during stance. This finding might be useful in most clinical settings when a single sensorized platform rather than a complete walkway is available.

Multiple Sclerosis (MS) is a chronic neurological disorder affecting the central nervous system that significantly impairs postural control and functional abilities. Robotic-assisted gait training mitigates this functional deterioration. This preliminary study aims to investigate the effects of a four-week gait training with the ExoAtlet II exoskeleton on static balance control and functional mobility in five individuals with MS (Expanded Disability Status Scale ≤ 2.5). Before and after the training, they were assessed in quiet standing under Eyes Open (EO) and Eyes Closed (EC) conditions and with the Timed Up and Go (TUG) test. Center of Pressure (CoP) Sway Area, Antero–Posterior (AP) and Medio–Lateral (ML) CoP displacement, Stay Time, and Total Instability Duration were computed. TUG test Total Duration, sit-to-stand, stand-to-sit, and linear walking phase duration were analyzed. To establish target reference values for rehabilitation advancement, the same evaluations were performed on a matched healthy cohort. After the training, an improvement in static balance with EO was observed towards HS values (reduced Sway Area, AP and ML CoP displacement, and Total Instability Duration and increased Stay Time). Enhancements under EC condition were less marked. TUG test performance improved, particularly in the stand-to-sit phase. These preliminary findings suggest functional benefits of exoskeleton gait training for individuals with MS.

Lower limb exoskeletons represent a relevant tool for rehabilitating gait in patients with lower limb movement disorders. Partial assistance exoskeletons adaptively provide the joint torque needed, on top of that produced by the patient, for a correct and stable gait, helping the patient to recover an autonomous gait. Thus, the device needs to identify the different phases of the gait cycle to produce precisely timed commands that drive its joint motors appropriately. In this study, EMG signals have been used for gait phase detection considering that EMG activations lead limb kinematics by at least 120 ms. We propose a deep learning model based on bidirectional LSTM to identify stance and swing gait phases from EMG data. We built a dataset of EMG signals recorded at 1500 Hz from four muscles from the dominant leg in a population of 26 healthy subjects walking overground (WO) and walking on a treadmill (WT) using a lower limb exoskeleton. The data were labeled with the corresponding stance or swing gait phase based on limb kinematics provided by inertial motion sensors. The model was studied in three different scenarios, and we explored its generalization abilities and evaluated its applicability to the online processing of EMG data. The training was always conducted on 500-sample sequences from WO recordings of 23 subjects. Testing always involved WO and WT sequences from the remaining three subjects. First, the model was trained and tested on 500 Hz EMG data, obtaining an overall accuracy on the WO and WT test datasets of 92.43% and 91.16%, respectively. The simulation of online operation required 127 ms to preprocess and classify one sequence. Second, the trained model was evaluated against a test set built on 1500 Hz EMG data. The accuracies were lower, yet the processing times were 11 ms faster. Third, we partially retrained the model on a subset of the 1500 Hz training dataset, achieving 87.17% and 89.64% accuracy on the 1500 Hz WO and WT test sets, respectively. Overall, the proposed deep learning model appears to be a valuable candidate for entering the control pipeline of a lower limb rehabilitation exoskeleton in terms of both the achieved accuracy and processing times.