A Novel Point-in-Polygon-Based sEMG Classifier for Hand Exoskeleton Systems

Integrated block-wise neural network with auto-learning search framework for finger gesture recognition using sEMG signals

Accurate finger gesture recognition with surface electromyography (sEMG) is essential and long-challenge in the muscle-computer interface, and many high-performance deep learning models have been developed to predict gestures. For these models, problem-specific tuning of network architecture is essential for improving the performance, yet it requires substantial knowledge of network architecture design and commitment of time and effort. This process thus imposes a major obstacle to the widespread and flexible application of modern deep learning. To address this issue, we present an auto-learning search framework (ALSF) to generate the integrated block-wised neural network (IBWNN) for sEMG-based gesture recognition. IBWNN contains several feature extraction blocks and dimensional reduction layers, and each feature extraction block integrates two sub-blocks (i.e., multi-branch convolutional block and triplet attention block). Meanwhile, ALSF generates optimal models for gesture recognition through the reinforcement learning method. The results show that the generated models yield state-of-the-art results compared to the modern popular networks on the open dataset Ninapro DB5. Moreover, compared to other networks, the generated models have fewer parameters and can be deployed in practical applications with less resource consumption.

Wearable Robots: An Original Mechatronic Design of a Hand Exoskeleton for Assistive and Rehabilitative Purposes

Robotic devices are being employed in more and more sectors to enhance, streamline, and augment the outcomes of a wide variety of human activities. Wearable robots arise indeed as of-vital-importance tools for telerehabilitation or home assistance targeting people affected by motor disabilities. In particular, the field of “Robotics for Medicine and Healthcare” is attracting growing interest. The development of such devices is a primarily addressed topic since the increasing number of people in need of rehabilitation or assistive therapies (due to population aging) growingly weighs on the healthcare systems of the nation. Besides, the necessity to move to clinics represents an additional logistic burden for patients and their families. Among the various body parts, the hand is specially investigated since it most ensures the independence of an individual, and thus, the restoration of its dexterity is considered a high priority. In this study, the authors present the development of a fully wearable, portable, and tailor-made hand exoskeleton designed for both home assistance and telerehabilitation. Its purpose is either to assist patients during activities of daily living by running a real-time intention detection algorithm or to be used for remotely supervised or unsupervised rehabilitation sessions by performing exercises preset by therapists. Throughout the mechatronic design process, special attention has been paid to the complete wearability and comfort of the system to produce a user-friendly device capable of assisting people in their daily life or enabling recorded home rehabilitation sessions allowing the therapist to monitor the state evolution of the patient. Such a hand exoskeleton system has been designed, manufactured, and preliminarily tested on a subject affected by spinal muscular atrophy, and some results are reported at the end of the article.