A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-02-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/2/202 |
| _version_ | 1828117822697373696 |
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| author | Lei Yu Daxi Xiong Liquan Guo Jiping Wang |
| author_facet | Lei Yu Daxi Xiong Liquan Guo Jiping Wang |
| author_sort | Lei Yu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T13:20:41Z |
| format | Article |
| id | doaj.art-ccf7737cae5e42f2b5480078ccfe4127 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:20:41Z |
| publishDate | 2016-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ccf7737cae5e42f2b5480078ccfe41272022-12-22T04:22:12ZengMDPI AGSensors1424-82202016-02-0116220210.3390/s16020202s16020202A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke PatientsLei Yu0Daxi Xiong1Liquan Guo2Jiping Wang3Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, ChinaJiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, ChinaJiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, ChinaJiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No. 88, Keling Road, Suzhou, Jiangsu 215163, ChinaClinical 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.http://www.mdpi.com/1424-8220/16/2/202compressed sensingwearable sensor networkquantitative assessmentstrokeBrunnstrom stage classification |
| spellingShingle | Lei Yu Daxi Xiong Liquan Guo Jiping Wang A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients Sensors compressed sensing wearable sensor network quantitative assessment stroke Brunnstrom stage classification |
| title | A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients |
| title_full | A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients |
| title_fullStr | A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients |
| title_full_unstemmed | A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients |
| title_short | A Compressed Sensing-Based Wearable Sensor Network for Quantitative Assessment of Stroke Patients |
| title_sort | compressed sensing based wearable sensor network for quantitative assessment of stroke patients |
| topic | compressed sensing wearable sensor network quantitative assessment stroke Brunnstrom stage classification |
| url | http://www.mdpi.com/1424-8220/16/2/202 |
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