title: A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients.
abstract: Clinical rehabilitation assessment is an important part of the therapy process because it is the premise for prescribing suitable rehabilitation interventions. However, the commonly used assessment scales have the following two drawbacks: (1) they are susceptible to subjective factors; (2) they only have several rating levels and are influenced by a ceiling effect, making it impossible to exactly detect any further improvement in the movement. Meanwhile, energy constraints are a primary design consideration in wearable sensor network systems since they are often battery-operated. Traditionally, for wearable sensor network systems that follow the Shannon/Nyquist sampling theorem, there are many data that need to be sampled and transmitted. This paper proposes a novel wearable sensor network system to monitor and quantitatively assess the upper limb motion function, based on compressed sensing technology. With the sparse representation model, less data is transmitted to the computer than with traditional systems. The experimental results show that the accelerometer signals of Bobath handshake and shoulder touch exercises can be compressed, and the length of the compressed signal is less than 1/3 of the raw signal length. More importantly, the reconstruction errors have no influence on the predictive accuracy of the Brunnstrom stage classification model. It also indicated that the proposed system can not only reduce the amount of data during the sampling and transmission processes, but also, the reconstructed accelerometer signals can be used for quantitative assessment without any loss of useful information.
authors: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, China. yuthreestone@gmail.com.|University of Chinese Academy of Sciences, Beijing 100049, China. yuthreestone@gmail.com.
authors: Lei
authors: L
authors: Yu
authors: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, China. xiongdx@sibet.ac.cn.
authors: Daxi
authors: D
authors: Xiong
authors: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, China. guolq@sibet.ac.cn.
authors: Liquan
authors: L
authors: Guo
authors: Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, China. wangjp@sibet.ac.cn.
authors: Jiping
authors: J
authors: Wang
mesh_terms: D015374:Biosensing Techniques / Q000295:instrumentation*; D005081:Exercise Therapy; D006801:Humans; D009068:Movement / Q000502:physiology; D020521:Stroke / Q000503:physiopathology / Q000628:therapy*; D000071939:Stroke Rehabilitation / Q000295:instrumentation*; D059015:Wireless Technology
keywords: Brunnstrom stage classification; compressed sensing; quantitative assessment; stroke; wearable sensor network