Eye-Movement-Controlled Wheelchair Based on Flexible Hydrogel Biosensor and WT-SVM.

Xiaoming Wang,Yineng Xiao,Hailiang Zhang,Fangming Deng,Yugen Chen

doi:10.3390/bios11060198

Xiaoming Wang, Yineng Xiao + Show 3 more

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https://doi.org/10.3390/bios11060198

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Journal: Biosensors	Publication Date: Jun 16, 2021
Citations: 18	License type: CC BY 4.0

Affiliation: East China Jiaotong University

Abstract

To assist patients with restricted mobility to control wheelchair freely, this paper presents an eye-movement-controlled wheelchair prototype based on a flexible hydrogel biosensor and Wavelet Transform-Support Vector Machine (WT-SVM) algorithm. Considering the poor deformability and biocompatibility of rigid metal electrodes, we propose a flexible hydrogel biosensor made of conductive HPC/PVA (Hydroxypropyl cellulose/Polyvinyl alcohol) hydrogel and flexible PDMS (Polydimethylsiloxane) substrate. The proposed biosensor is affixed to the wheelchair user’s forehead to collect electrooculogram (EOG) and strain signals, which are the basis to recognize eye movements. The low Young’s modulus (286 KPa) and exceptional breathability (18 g m−2 h−1 of water vapor transmission rate) of the biosensor ensures a conformal and unobtrusive adhesion between it and the epidermis. To improve the recognition accuracy of eye movements (straight, upward, downward, left, and right), the WT-SVM algorithm is introduced to classify EOG and strain signals according to different features (amplitude, duration, interval). The average recognition accuracy reaches 96.3%, thus the wheelchair can be manipulated precisely.

Highlights

As for voice control, the speech commands are susceptible to ambient noise, which greatly reduces the practicality in noisy environments [10]
In order to determine whether the designed biosensor with the preferred dimensions can adapt to the deformation of epidermis, we studied its load-deflection characteristic
(2)).to determine whether the designed biosensor with the preferred dimensions can adapt to the deformation of epidermis, we studied its load-deflection characteristic π ET

Summary

Introduction

Data from the World Health Organization (WHO) indicate that wheelchairs are indispensable for 75 million people on a daily basis, which accounts for 1% of the world’s total population [1]. Those who are highly handicapped (e.g., ALS patients) are incapable of maneuvering their wheelchairs manually. The main challenge for hand gesture control is the laborious operation and misrecognition of gestures [9]. It is not applicable for people with limited limb movement. Multiple studies have demonstrated the utilization of video-based eye-tracking systems [11,12,13]

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