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Yang B, Li Y, Wang F, Auyeung S, Leung M, Mak M, Tao X. Intelligent wearable system with accurate detection of abnormal gait and timely cueing for mobility enhancement of people with Parkinson's disease. Wearable Technol 2022; 3:e12. [PMID: 38486907 PMCID: PMC10936378 DOI: 10.1017/wtc.2022.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/11/2022] [Accepted: 05/25/2022] [Indexed: 03/17/2024]
Abstract
Previously reported wearable systems for people with Parkinson's disease (PD) have been focused on the detection of abnormal gait. They suffered from limited accuracy, large latency, poor durability, comfort, and convenience for daily use. Herewith we report an intelligent wearable system (IWS) that can accurately detect abnormal gait in real-time and provide timely cueing for PD patients. The system features novel sensitive, comfortable and durable plantar pressure sensing insoles with a highly compressed data set, an accurate and fast gait algorithm, and wirelessly controlled timely sensory cueing devices. A total of 29 PD patients participated in the first phase without cueing for developing processes of the algorithm, which achieved an accuracy of over 97% for off-line detection of freezing of gait (FoG). In the second phase with cueing, the evaluation of the whole system was conducted with 16 PD subjects via trial and a questionnaire survey. This system demonstrated an accuracy of 94% for real-time detection of FoG and a mean latency of 0.37 s between the onset of FoG and cueing activation. In questionnaire survey, 88% of the PD participants confirmed that this wearable system could effectively enhance walking, 81% thought that the system was comfortable and convenient, and 70% overcame the FoG. Therefore, the IWS makes it an effective, powerful, and convenient tool for enhancing the mobility of people with PD.
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Affiliation(s)
- Bao Yang
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Ying Li
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
| | - Fei Wang
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- School of Textile Materials and Engineering, Wuyi University, Jiangmen, China
| | - Stephanie Auyeung
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Manyui Leung
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
| | - Margaret Mak
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xiaoming Tao
- Institute of Textiles and Clothing, The Hong Kong Polytechnic University, Hong Kong, China
- Research Institute for Intelligent Wearable Systems, The Hong Kong Polytechnic University, Hong Kong, China
- Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen, China
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