원문정보
한국차세대컴퓨팅학회
한국차세대컴퓨팅학회 학술대회
The 10th International Conference on Next Generation Computing 2024
2024.11
pp.254-257
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
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자료제공 : 네이버학술정보