Earticle

초록

영어

This paper introduces a system that uses Functional Electrical Stimulation (FES) for finger flexion control aimed rehabilitation for stroke patients. To address the variability in electrode between patients, Reinforcement Learning is applied together with a switching network that allows automatic electrode selection. This results in an adaptable system that does not require rigorous searching of the patient’s optimal stimulation points. Data that supports the differences in the stimulation location for individuals as well as the ability of the system to converge automatically to a stimulation point is presented.

목차

Abstract
I. INTRODUCTION
II. FES REHABILITATIVE DEVICE SYSTEM
A. Stimulation Signal Generation and Electrode Selection Switching
B. IMU-based Feedback System
C. Reinforcement Learning based Electrode Selection System
III. DATA COLLECTION AND TESTING PROTOCOLS
IV. RESULTS AND DISCUSSIONS
A. Variability of Electrode Pair Locations in Finger Flexion over time
B. Electrode Matrix Displacement Effects
C. Initial Conditions and Convergence
V. SUMMARY
REFERENCES

키워드

저자

• Luis Gerardo Canete, Jr. [ Department of Computer Engineering University of San Carlos Cebu City, Philippines ]
• Clyde Matthew Condor [ Department of Computer Engineering University of San Carlos Cebu City, Philippines ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004