sEMG Based Human Motion Intention Recognition
Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better h...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2019/3679174 |
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| _version_ | 1832551379468550144 |
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| author | Li Zhang Geng Liu Bing Han Zhe Wang Tong Zhang |
| author_facet | Li Zhang Geng Liu Bing Han Zhe Wang Tong Zhang |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | Human motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented. |
| format | Article |
| id | doaj-art-1dad9de559e94b3ebe4db44aef934132 |
| institution | Kabale University |
| issn | 1687-9600 1687-9619 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Robotics |
| spelling | doaj-art-1dad9de559e94b3ebe4db44aef9341322025-02-03T06:01:36ZengWileyJournal of Robotics1687-96001687-96192019-01-01201910.1155/2019/36791743679174sEMG Based Human Motion Intention RecognitionLi Zhang0Geng Liu1Bing Han2Zhe Wang3Tong Zhang4Shaanxi Engineering Laboratory for Transmissions and Controls, Northwestern Polytechnical University, Xi’an, ChinaShaanxi Engineering Laboratory for Transmissions and Controls, Northwestern Polytechnical University, Xi’an, ChinaShaanxi Engineering Laboratory for Transmissions and Controls, Northwestern Polytechnical University, Xi’an, ChinaShaanxi Engineering Laboratory for Transmissions and Controls, Northwestern Polytechnical University, Xi’an, ChinaShaanxi Engineering Laboratory for Transmissions and Controls, Northwestern Polytechnical University, Xi’an, ChinaHuman motion intention recognition is a key to achieve perfect human-machine coordination and wearing comfort of wearable robots. Surface electromyography (sEMG), as a bioelectrical signal, generates prior to the corresponding motion and reflects the human motion intention directly. Thus, a better human-machine interaction can be achieved by using sEMG based motion intention recognition. In this paper, we review and discuss the state of the art of the sEMG based motion intention recognition that is mainly used in detail. According to the method adopted, motion intention recognition is divided into two groups: sEMG-driven musculoskeletal (MS) model based motion intention recognition and machine learning (ML) model based motion intention recognition. The specific models and recognition effects of each study are analyzed and systematically compared. Finally, a discussion of the existing problems in the current studies, major advances, and future challenges is presented.http://dx.doi.org/10.1155/2019/3679174 |
| spellingShingle | Li Zhang Geng Liu Bing Han Zhe Wang Tong Zhang sEMG Based Human Motion Intention Recognition Journal of Robotics |
| title | sEMG Based Human Motion Intention Recognition |
| title_full | sEMG Based Human Motion Intention Recognition |
| title_fullStr | sEMG Based Human Motion Intention Recognition |
| title_full_unstemmed | sEMG Based Human Motion Intention Recognition |
| title_short | sEMG Based Human Motion Intention Recognition |
| title_sort | semg based human motion intention recognition |
| url | http://dx.doi.org/10.1155/2019/3679174 |
| work_keys_str_mv | AT lizhang semgbasedhumanmotionintentionrecognition AT gengliu semgbasedhumanmotionintentionrecognition AT binghan semgbasedhumanmotionintentionrecognition AT zhewang semgbasedhumanmotionintentionrecognition AT tongzhang semgbasedhumanmotionintentionrecognition |