Computer Science Department, Università degli Studi di Milano, Milan, Italy.
Methods Mol Biol. 2021;2190:337-354. doi: 10.1007/978-1-0716-0826-5_17.
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.
神经退行性疾病，主要包括肌萎缩侧索硬化症、帕金森病、阿尔茨海默病和更罕见的疾病，由于它们对医疗保健为公共服务的国家的经济产生影响而引起了医疗服务提供者的关注。这些疾病随着年龄的增长而增加，影响大脑的运动神经元和认知区域，导致受其影响的人严重残疾。早期预测这些综合征是要实施的第一个策略，然后开发可以恢复运动和主要认知功能的假肢。假肢可以恢复一些重要的残疾，如运动和失语症，降低辅助成本，并提高受神经退行性疾病影响的人的生活质量。由于人工智能 (AI)（深度学习、受大脑启发的计算范例、非线性预测、神经模糊建模）领域的最新进展，使用最先进的计算技术可以早期预测神经退行性疾病。最新一代的人工神经网络 (ANN) 利用在线学习、快速训练、高级知识表示、在线进化、数据学习和推断规则等功能。可穿戴电子产品也在迅速发展，是使用基于 AI 的模型为神经退行性疾病和智能假肢的早期预测部署物理和实际（非侵入性）设备的重要使能技术。在这里，我们描述如何应用高级的受大脑启发的推理和预测方法、演进模糊神经网络 (EFuNN) 范例和尖峰神经网络 (SNN) 范例，以及开发用于功能康复的可穿戴电子假肢的系统要求。
