Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

Abstract

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.

Personalization of the 3D-Printed Upper Limb Exoskeleton Design—Mechanical and IT Aspects

“…Surface electromyography (sEMG) and machine learning-based adaptive controllers of upper limb exoskeletons require personalization. They have achieved an average accuracy of over 93% for users with muscular dystrophy and neurodegenerative diseases without previous exposure to these types of controllers [14]. Upper limb exoskeletons support individually selected neuromotor rehabilitation including restoring the function of the upper limb and improving the quality of life of patients.…”

On the Applications of EMG Sensors and Signals

“…Robotic exoskeleton driven by EMG has received significant attention in the literature. One study in the Special Issue [ 17 ] proposed an artificial neural network-trained adaptive controller mechanism to navigate a wheelchair-mounted upper limb robotic exoskeleton. The EMG from upper limb muscles informs users’ intentions, and thus are the input sources to the system.…”

Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor

“…The EMG-based methods can either be correlated with the muscular groups responsible for the intended movements [13,14] or with other groups [15]. Among others, the latter gives the opportunity to control the devices supporting impaired extremities by the other hand's gestures [15]. On the contrary, some of the described methods aim at recalculating registered EMG into joint torques [16].…”