Toward Sustainable and Accessible Mobility: A Functional Electrical Stimulation-Based Robotic Bike With a Fatigue-Compensation Algorithm and Mechanism for Cybathlon 2020

The functional electrical stimulation (FES) bike race at Cybathlon is a competition in which a pilot with a spinal cord injury (SCI) drives a robotic bike with using his/her paralyzed muscles. The intelligent technologies embedded in the robotic bike stimulate the paralyzed leg muscles and allow the...

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Bibliographic Details
Published in:IEEE robotics & automation magazine 2021-12, Vol.28 (4), p.32-42
Main Authors: Kim, Yeongjin, Lee, Seung Ryeol, Kim, Sungjun, de Sa Rosa, Tuani, Gong, Yujin, Park, Chaneun, Wang, Jae-Han, Park, Kiwon, Kim, Jung-Yup, Shin, Dongjun
Format: Article
Language:English
Subjects:
Algorithms
Bicycles
Compensation
Energy consumption
Fatigue
Legged locomotion
Muscle fatigue
Muscles
Muscular fatigue
Neuromuscular stimulation
Paralysis
Predictive models
Rehabilitation
Robotics
Robots
Spinal cord injuries
Spinal cord injury
Stimulation
Sustainable development
Training data
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Summary:The functional electrical stimulation (FES) bike race at Cybathlon is a competition in which a pilot with a spinal cord injury (SCI) drives a robotic bike with using his/her paralyzed muscles. The intelligent technologies embedded in the robotic bike stimulate the paralyzed leg muscles and allow these muscles to actuate again. This advanced equipment enriches people's lives by expanding their mobility for rehabilitation and leisure. However, due to the energy consumption of human muscles, muscle fatigue should be properly managed to maintain high speeds for long durations. In this article, we propose three methods to manage muscle fatigue: 1) a stimulation-control scheme based on fatigue prediction, 2) a human-vehicle smart interface, and 3) a muscular hypertrophy program. Without a fatigue-compensation algorithm, a pilot traveled only roughly 200 m in 3 min due to muscle fatigue; however, using the proposed methods, the pilot was able to travel 1,200 m in 3 min 53 s, after training for only three months.
ISSN:1070-9932
1558-223X
DOI:10.1109/MRA.2021.3111835