Soft, Lightweight Wearable Robots to Support the Upper Limb in Activities of Daily Living: A Feasibility Study on Chronic Stroke Patients.

Stroke can be a devastating condition that impairs the upper limb and reduces mobility. Wearable robots can aid impaired users by supporting performance of Activities of Daily Living (ADLs). In the past decade, soft devices have become popular due to their inherent malleable and low-weight properties that makes them generally safer and more ergonomic. In this study, we present an improved version of our previously developed gravity-compensating upper limb exosuit and introduce a novel hand exoskeleton. The latter uses 3D-printed structures that are attached to the back of the fingers which prevent undesired hyperextension of joints. We explored the feasibility of using this integrated system in a sample of 10 chronic stroke patients who performed 10 ADLs. We observed a significant reduction of 30.3 ± 3.5% (mean ± standard error), 31.2 ± 3.2% and 14.0 ± 5.1% in the mean muscular activity of the Biceps Brachii (BB), Anterior Deltoid (AD) and Extensor Digitorum Communis muscles, respectively. Additionally, we observed a reduction of 14.0 ± 11.5%, 14.7 ± 6.9% and 12.8 ± 4.4% in the coactivation of the pairs of muscles BB and Triceps Brachii (TB), BB and AD, and TB and Pectoralis Major (PM), respectively, typically associated to pathological muscular synergies, without significant degradation of healthy muscular coactivation. There was also a significant increase of elbow flexion angle ( 12.1±1.5° ). These results further cement the potential of using lightweight wearable devices to assist impaired users.

Elbow Motion Trajectory Prediction Using a Multi-Modal Wearable System: A Comparative Analysis of Machine Learning Techniques.

Motion intention detection is fundamental in the implementation of human-machine interfaces applied to assistive robots. In this paper, multiple machine learning techniques have been explored for creating upper limb motion prediction models, which generally depend on three factors: the signals collected from the user (such as kinematic or physiological), the extracted features and the selected algorithm. We explore the use of different features extracted from various signals when used to train multiple algorithms for the prediction of elbow flexion angle trajectories. The accuracy of the prediction was evaluated based on the mean velocity and peak amplitude of the trajectory, which are sufficient to fully define it. Results show that prediction accuracy when using solely physiological signals is low, however, when kinematic signals are included, it is largely improved. This suggests kinematic signals provide a reliable source of information for predicting elbow trajectories. Different models were trained using 10 algorithms. Regularization algorithms performed well in all conditions, whereas neural networks performed better when the most important features are selected. The extensive analysis provided in this study can be consulted to aid in the development of accurate upper limb motion intention detection models.

IMU-based assistance modulation in upper limb soft wearable exosuits.

Soft exosuits have advantages over their rigid counterparts in terms of portability, transparency and ergonomics. Our previous work has shown that a soft, fabric-based exosuit, actuated by an electric motor and a Bowden cable, reduced the muscular effort of the user when flexing the elbow. This previous exosuit used a gravity compensation algorithm with the assumption that the shoulder was adducted at the trunk. In this investigation, the shoulder elevation angle was incorporated into the gravity compensation control via inertial measurement units (IMUs). We assessed our updated gravity compensation model with four healthy, male subjects (age: $26.2 \pm 1.19$ years) who followed an elbow flexion reference trajectory which reached three amplitudes $(25^{\circ}, 50^{\circ}, 75^{\circ})$ and was repeated at three shoulder angles $(25^{\circ}, 50^{\circ}, 75^{\circ})$. To assess the performance of the exosuit; the smoothness, tracking accuracy and muscle activity were investigated during each motion. We found a reduction of biceps brachii activation (24.3%) in the powered condition compared to the unpowered condition. In addition, there was an improvement in kinematic smoothness (0.83%) and a reduction of tracking accuracy (26.5%) in the powered condition with respect to the unpowered condition. We can conclude that the updated gravity compensation algorithm has increased the number of supported movements by considering the shoulder elevation, which has improved the usability of the device.