Telemedical System for Monitoring the Psycho-Neurological State of Patients in the Process of Rehabilitation

Sima Das, P. Bhowmick, N. Giri, K. Minakova, O. Rubanenko, D. Danylchenko
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Abstract

Telemedical system for monitoring the psycho-neurological state for rehabilitation is evolving for assessment and supervision of various neurological syndromes. Persons with disabilities are not a homogenous group, they are facing multiple problems in their daily life. Common problem of people with disabilities and old aged people is that they have lacked access to basic services. Nowadays researchers are focused on human computer interaction-based rehabilitation technologies that bring social-emotional intelligence closer. The paper is designed to achieve cognitive rehabilitation using machine learning approaches for disabled and elderly people. Electroencephalograms are used to monitor brain activity of the human brain and Kinect sensors are used to track users' movements. Chebyshev filter used to remove noise, for feature extraction Autoencoder technique is used, and classification is done by Transfer learning based Convolutional neural network with 95% and above accuracy. The proposed system will be applied in real time to achieve a better quality of life for disabled and elderly people.
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康复过程中患者心理神经状态监测的远程医疗系统
远程医疗系统监测心理-神经状态的康复正在发展,以评估和监督各种神经综合征。残疾人不是一个单一的群体,他们在日常生活中面临着多种问题。残疾人和老年人的共同问题是缺乏获得基本服务的机会。目前,研究人员关注的是基于人机交互的康复技术,这种技术可以拉近社交情商的距离。该论文旨在利用机器学习方法实现残疾人和老年人的认知康复。脑电图用于监测人脑的活动,Kinect传感器用于跟踪用户的动作。采用切比雪夫滤波去除噪声,特征提取采用自编码器技术,分类采用基于迁移学习的卷积神经网络,准确率在95%以上。建议的系统将实时应用,以提高残疾人和老年人的生活质量。
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