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A laser-engraved wearable gait recognition sensor system for exoskeleton robots.
Sun, Maowen; Cui, Songya; Wang, Zezheng; Luo, Huayu; Yang, Huayong; Ouyang, Xiaoping; Xu, Kaichen.
Afiliação
  • Sun M; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
  • Cui S; School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, 310015 China.
  • Wang Z; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
  • Luo H; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
  • Yang H; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
  • Ouyang X; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
  • Xu K; State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027 China.
Microsyst Nanoeng ; 10: 50, 2024.
Article em En | MEDLINE | ID: mdl-38595947
ABSTRACT
As a reinforcement technology that improves load-bearing ability and prevents injuries, assisted exoskeleton robots have extensive applications in freight transport and health care. The perception of gait information by such robots is vital for their control. This information is the basis for motion planning in assistive and collaborative functions. Here, a wearable gait recognition sensor system for exoskeleton robots is presented. Pressure sensor arrays based on laser-induced graphene are developed with flexibility and reliability. Multiple sensor units are integrated into an insole to detect real-time pressure at key plantar positions. In addition, the circuit hardware and the algorithm are designed to reinforce the sensor system with the capability of gait recognition. The experimental results show that the accuracy of gait recognition by the proposed system is 99.85%, and the effectiveness of the system is further verified through testing on an exoskeleton robot.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Microsyst Nanoeng Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Microsyst Nanoeng Ano de publicação: 2024 Tipo de documento: Article