Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals
To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/4920750 |
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| author | Ning Wang Yang Xu Hongbin Ma Xiaofeng Liu |
| author_facet | Ning Wang Yang Xu Hongbin Ma Xiaofeng Liu |
| author_sort | Ning Wang |
| collection | DOAJ |
| description | To investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists. |
| format | Article |
| id | doaj-art-7ec81c01c4ac425d8a7bb91343f15d05 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7ec81c01c4ac425d8a7bb91343f15d052025-02-03T05:46:20ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/49207504920750Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG SignalsNing Wang0Yang Xu1Hongbin Ma2Xiaofeng Liu3School of Computing, Electronics and Mathematics, Plymouth University, Drake Circus, Plymouth PL4 8AA, UKKey Laboratory of Autonomous Systems and Networked Control, College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Automation, Beijing Institute of Technology, Zhong Guan Cun South Street, Haidian District, Beijing 100081, ChinaChangzhou Key Laboratory of Robotics and Intelligent Technology, Changzhou 213022, ChinaTo investigate the effects of muscle fatigue on bioinspired robot learning quality in teaching by demonstration (TbD) tasks, in this work, we propose to first identify the emerging muscle fatigue phenomenon of the human demonstrator by analyzing his/her surface Electromyography (sEMG) recordings and then guide the robot learning curve with this knowledge in mind. The time-varying amplitude and frequency sequences determining the subband sEMG signals have been estimated and their dominant values over short time intervals have been explored as fatigue-indicating features. These features are found carrying muscle fatigue cues of the human demonstrator in the course of robot manipulation. In robot learning tasks requiring multiple demonstrations, the fatiguing status of human demonstrator can be acquired by tracking the changes of the proposed features over time. In order to model data from multiple demonstrations, Gaussian mixture models (GMMs) have been employed. According to the identified muscle fatigue factor, a weight has been assigned to each of the demonstration trials in training stage, which is therefore termed as weighted GMMs (W-GMMs) algorithm. Six groups of data with various fatiguing status, as well as their corresponding weights, are taken as input data to get the adapted W-GMMs parameters. After that, Gaussian mixture regression (GMR) algorithm has been applied to regenerate the movement trajectory for the robot. TbD experiments on Baxter robot with 30 human demonstration trials show that the robot can successfully accomplish the taught task with a generated trajectory much closer to that of the desirable condition where little fatigue exists.http://dx.doi.org/10.1155/2018/4920750 |
| spellingShingle | Ning Wang Yang Xu Hongbin Ma Xiaofeng Liu Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals Complexity |
| title | Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals |
| title_full | Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals |
| title_fullStr | Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals |
| title_full_unstemmed | Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals |
| title_short | Exploration of Muscle Fatigue Effects in Bioinspired Robot Learning from sEMG Signals |
| title_sort | exploration of muscle fatigue effects in bioinspired robot learning from semg signals |
| url | http://dx.doi.org/10.1155/2018/4920750 |
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