AI-Based Methods and Technologies to Develop Wearable Devices for Prosthetics and Predictions of Degenerative Diseases

Abstract

Neurodegenerative diseases, mainly amyotrophic lateral sclerosis, Parkinson, Alzheimer, and rarer diseases, have gained the attention of healthcare service providers due to their impact on the economy of countries where healthcare is a public service. These diseases increase with aging and affect the neuromotor cells and cognitive areas in the brain, causing serious disabilities in people affected by them. Early prediction of these syndromes is the first strategy to be implemented, then the developing of prostheses that rehabilitate motion and the primary cognitive functions. Prostheses could recover some important disabilities such as motion and aphasia, reduce the cost of assistance and increase the life quality of people affected by neurodegenerative diseases. Due to recent advances in the field of artificial intelligence (AI) (deep learning, brain-inspired computational paradigms, nonlinear predictions, neuro-fuzzy modeling), the early prediction of neurodegenerative diseases is possible using state-of-the-art computational technologies. The latest generation of artificial neural networks (ANNs) exploits capabilities such as online learning, fast training, high level knowledge representation, online evolution, learning by data and inferring rules. Wearable electronics is also developing rapidly and represents an important enabling technology to deploy physical and practical (noninvasive) devices using AI-based models for early prediction of neurodegenerative diseases and of intelligent prostheses. Here we describe how to apply advanced brain-inspired methods for inference and prediction, the evolving fuzzy neural network (EFuNN) paradigm and the spiking neural network (SNN) paradigm, and the system requirements to develop a wearable electronic prosthesis for functional rehabilitation.