A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing
This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8873561/ |
| _version_ | 1818416014718664704 |
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| author | Hee Young Chae Kwangmuk Lee Jonggyu Jang Kyeonghwan Park Jae Joon Kim |
| author_facet | Hee Young Chae Kwangmuk Lee Jonggyu Jang Kyeonghwan Park Jae Joon Kim |
| author_sort | Hee Young Chae |
| collection | DOAJ |
| description | This paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-μm complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time handgesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis. |
| first_indexed | 2024-12-14T11:44:09Z |
| format | Article |
| id | doaj.art-d6607d30bd194f78a7ec5af9eba985da |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T11:44:09Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-d6607d30bd194f78a7ec5af9eba985da2022-12-21T23:02:42ZengIEEEIEEE Access2169-35362019-01-01715132015132810.1109/ACCESS.2019.29480908873561A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet PreprocessingHee Young Chae0Kwangmuk Lee1Jonggyu Jang2Kyeonghwan Park3Jae Joon Kim4https://orcid.org/0000-0003-4581-4115School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaSchool of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaSchool of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaSchool of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaSchool of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South KoreaThis paper presents a wearable wireless surface electromyogram (sEMG) integrated interface that utilizes a proposed analog pseudo-wavelet preprocessor (APWP) for signal acquisition and pattern recognition. The APWP is integrated into a readout integrated circuit (ROIC), which is fabricated in a 0.18-μm complementary metal-oxide-semiconductor (CMOS) process. Based on this ROIC, a wearable device module and its wireless system prototype are implemented to recognize five kinds of real-time handgesture motions, where the power consumption is further reduced by adopting low-power components. Real-time measurements of sEMG signals and APWP data through this wearable interface are wirelessly transferred to a laptop or a sensor hub, and then they are further processed to implement the pseudo-wavelet transform under the MATLAB environment. The resulting APWP-augmented pattern-recognition algorithm was experimentally verified to improve the accuracy by 7 % with a real-time frequency analysis.https://ieeexplore.ieee.org/document/8873561/Surface electromyogrampattern recognitionreadout integrated circuitanalog wavelet preprocessorwireless sensor interface |
| spellingShingle | Hee Young Chae Kwangmuk Lee Jonggyu Jang Kyeonghwan Park Jae Joon Kim A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing IEEE Access Surface electromyogram pattern recognition readout integrated circuit analog wavelet preprocessor wireless sensor interface |
| title | A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing |
| title_full | A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing |
| title_fullStr | A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing |
| title_full_unstemmed | A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing |
| title_short | A Wearable sEMG Pattern-Recognition Integrated Interface Embedding Analog Pseudo-Wavelet Preprocessing |
| title_sort | wearable semg pattern recognition integrated interface embedding analog pseudo wavelet preprocessing |
| topic | Surface electromyogram pattern recognition readout integrated circuit analog wavelet preprocessor wireless sensor interface |
| url | https://ieeexplore.ieee.org/document/8873561/ |
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