TY - GEN
T1 - Morphology Design of Soft Strain Sensors with Superior Stability for Wearable Rehabilitation Robots
AU - Wang, Qian
AU - Ofori, Seyram
AU - Liu, Qiulei
AU - Yu, Haoyong
AU - Ding, Shuo
AU - Yang, Haitao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
PY - 2023
Y1 - 2023
N2 - Accurate human motion tracking by wearable sensors is critical for wearable robots in rehabilitation, but existing sensing technologies have several limitations, such as unaffordability, poor stability, and reliability concerns. Through the sensor morphology design, this research focuses on the development of robust soft strain sensors using crumpled single-walled carbon nanotubes (SWCNTs). Compared to planar sensors, crumpled SWCNTs sensors exhibit wide working strain ranges, robust cycling performance, and superior mechanical stability. These sensors were integrated into a rehabilitation exoskeleton and successfully monitored elbow deformation and muscle activity by sensitive, stable, and reliable signals, indicating great potential in replacing EMG and inertial sensors to provide accurate and immediate feedback for optimized operations in rehabilitation tasks. This technology provides a cost-effective, wearable, and privacy-friendly solution for motion monitoring in rehabilitation robots, improving the effectiveness and convenience of rehabilitation treatment for people with physical disabilities.
AB - Accurate human motion tracking by wearable sensors is critical for wearable robots in rehabilitation, but existing sensing technologies have several limitations, such as unaffordability, poor stability, and reliability concerns. Through the sensor morphology design, this research focuses on the development of robust soft strain sensors using crumpled single-walled carbon nanotubes (SWCNTs). Compared to planar sensors, crumpled SWCNTs sensors exhibit wide working strain ranges, robust cycling performance, and superior mechanical stability. These sensors were integrated into a rehabilitation exoskeleton and successfully monitored elbow deformation and muscle activity by sensitive, stable, and reliable signals, indicating great potential in replacing EMG and inertial sensors to provide accurate and immediate feedback for optimized operations in rehabilitation tasks. This technology provides a cost-effective, wearable, and privacy-friendly solution for motion monitoring in rehabilitation robots, improving the effectiveness and convenience of rehabilitation treatment for people with physical disabilities.
KW - Crumpled microstructures
KW - Rehabilitation robots
KW - Sensor stability
KW - Strain sensor
UR - http://www.scopus.com/inward/record.url?scp=85175963069&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-6489-5_47
DO - 10.1007/978-981-99-6489-5_47
M3 - 会议稿件
AN - SCOPUS:85175963069
SN - 9789819964888
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 576
EP - 583
BT - Intelligent Robotics and Applications - 16th International Conference, ICIRA 2023, Proceedings
A2 - Yang, Huayong
A2 - Zou, Jun
A2 - Yang, Geng
A2 - Ouyang, Xiaoping
A2 - Liu, Honghai
A2 - Wang, Zhiyong
A2 - Yin, Zhouping
A2 - Liu, Lianqing
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International Conference on Intelligent Robotics and Applications, ICIRA 2023
Y2 - 5 July 2023 through 7 July 2023
ER -