A Control and Posture Recognition Strategy for Upper-Limb Rehabilitation of Stroke Patients

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A Control and Posture Recognition Strategy for Upper-Limb Rehabilitation of Stroke Patients

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Xian Yu,¹ Bo Xiao,² Ye Tian,³ Zihao Wu,⁴ Qi Liu,⁴ Jun Wang,⁴ Mingxu Sun,⁵ and Xiaodong Liu⁶

¹NARI Group Co., Ltd. (State Grid Electric Power Research Institute Co., Ltd.), Nanjing 211000, China

²School of Chemistry and Materials, Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China

³China Information Communication Technologies Group Corporation (CICT), Wuhan, China

⁴School of Computer and Software, Nanjing University of Information Science and Technology, 210044 Nanjing, China

⁵School of Electrical Engineering, University of Jinan, China

⁶School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh EH10 5DT, UK

Academic Editor: Jun Cai

Received18 Nov 2020

Revised08 Feb 2021

Accepted30 Mar 2021

Published18 May 2021

Abstract

At present, the study of upper-limb posture recognition is still in the primary stage; due to the diversity of the objective environment and the complexity of the human body posture, the upper-limb posture has no public dataset. In this paper, an upper extremity data acquisition system is designed, with a three-channel data acquisition mode, collect acceleration signal, and gyroscope signal as sample data. The datasets were preprocessed with deweighting, interpolation, and feature extraction. With the goal of recognizing human posture, experiments with KNN, logistic regression, and random gradient descent algorithms were conducted. In order to verify the superiority of each algorithm, the data window was adjusted to compare the recognition speed, computation time, and accuracy of each classifier. For the problem of improving the accuracy of human posture recognition, a neural network model based on full connectivity is developed. In addition, this paper proposes a finite state machine- (FSM-) based FES control model for controlling the upper limb to perform a range of functional tasks. In the process of constructing the network model, the effects of different hidden layers, activation functions, and optimizers on the recognition rate were experimental for the comparative analysis; the softplus activation function with better recognition performance and the adagrad optimizer are selected. Finally, by comparing the comprehensive recognition accuracy and time efficiency with other classification models, the fully connected neural network is verified in the human posture superiority in identification.

1. Introduction

There are more than 10 million new strokes per year worldwide [1], and stroke is still the leading cause of death and disability among adults [2]. With the accelerating aging of the society and the prevalence of unhealthy lifestyles, stroke diseases have shown explosive growth and are getting younger. Strokes are characterized by high incidence and disability, with World Health Organization data showing that strokes have a disability rate of up to 80%. The economic burden is 10 times greater than that of myocardial infarction. Therefore, prevention and treatment are urgent, and the rehabilitation system for patients needs to be improved.

Stroke patients’ recovery of limb function is one of the most important aspects of rehabilitation. At present, there are several different types of rehabilitation therapy in clinic, such as electromyographic feedback therapy, electrical stimulation therapy, and motor imagery mental training therapy, while the most highly regarded in clinical practice is functional electrical functional electrical stimulation (FES), with stimulation electrodes worn on the limbs of stroke patients consisting of the controller send out stimulation signals to electrically stimulate specific muscles to enable the limb to perform various types of functional rehabilitation or to perform daily activity, which in turn leads to the recovery of limb function. Stroke patients need to perform specific functional tasks in the process of rehabilitation, so an efficient control strategy needs to be designed. At the same time, due to the lack of existing public datasets, it is urgent to establish a database, design algorithms to analyze sensor device data, and identify the upper-limb posture movement of stroke patients. This can provide reference for the rehabilitation and rehabilitation effect of stroke patients. It provides an effective solution.

The paper is divided as follows: Section 2 presents the related work on this field. Section 3 and Section 4 demonstrate the methodologies. Section 5 shows the results and discusses the findings. Finally, Section 6 concludes the paper.