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Sensors 2016, 16(2), 202; doi:10.3390/s16020202

A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients

1
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
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editor: Leonhard M. Reindl
Received: 12 December 2015 / Revised: 23 January 2016 / Accepted: 3 February 2016 / Published: 5 February 2016
View Full-Text   |   Download PDF [3492 KB, uploaded 5 February 2016]   |  

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. View Full-Text
Keywords: compressed sensing; wearable sensor network; quantitative assessment; stroke; Brunnstrom stage classification compressed sensing; wearable sensor network; quantitative assessment; stroke; Brunnstrom stage classification
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Yu, L.; Xiong, D.; Guo, L.; Wang, J. A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients. Sensors 2016, 16, 202.

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