Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only
Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walk...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/12/4204 |
| _version_ | 1797529634965815296 |
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| author | Pankwon Kim Jinkyu Lee Choongsoo S. Shin |
| author_facet | Pankwon Kim Jinkyu Lee Choongsoo S. Shin |
| author_sort | Pankwon Kim |
| collection | DOAJ |
| description | Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals. |
| first_indexed | 2024-03-10T10:16:32Z |
| format | Article |
| id | doaj.art-664d8e34c5e4488296b57396968f2571 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T10:16:32Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-664d8e34c5e4488296b57396968f25712023-11-22T00:47:24ZengMDPI AGSensors1424-82202021-06-012112420410.3390/s21124204Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors OnlyPankwon Kim0Jinkyu Lee1Choongsoo S. Shin2Department of Mechanical Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, KoreaDepartment of Mechanical Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, KoreaDepartment of Mechanical Engineering, Sogang University, 35 Baekbeom-ro, Mapo-gu, Seoul 04107, KoreaClassification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although surface electromyography (sEMG) signals have been combined with inputs from other sensors to detect walking intention, no study has yet classified walking environments using sEMG only. Therefore, the purpose of this study is to classify the current walking environment based on the entire sEMG profile gathered from selected muscles in the lower extremities. The muscle activations of selected muscles in the lower extremities were measured in 27 participants while they walked over flat-ground, upstairs, downstairs, uphill, and downhill. An artificial neural network (ANN) was employed to classify these walking environments using the entire sEMG profile recorded for all muscles during the stance phase. The result shows that the ANN was able to classify the current walking environment with high accuracy of 96.3% when using activation from all muscles. When muscle activation from flexor/extensor groups in the knee, ankle, and metatarsophalangeal joints were used individually to classify the environment, the triceps surae muscle activation showed the highest classification accuracy of 88.9%. In conclusion, a current walking environment was classified with high accuracy using an ANN based on only sEMG signals.https://www.mdpi.com/1424-8220/21/12/4204surface electromyography (sEMG)deep learningnon-handcrafted featurewalking environmentsartificial neural network |
| spellingShingle | Pankwon Kim Jinkyu Lee Choongsoo S. Shin Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only Sensors surface electromyography (sEMG) deep learning non-handcrafted feature walking environments artificial neural network |
| title | Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only |
| title_full | Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only |
| title_fullStr | Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only |
| title_full_unstemmed | Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only |
| title_short | Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only |
| title_sort | classification of walking environments using deep learning approach based on surface emg sensors only |
| topic | surface electromyography (sEMG) deep learning non-handcrafted feature walking environments artificial neural network |
| url | https://www.mdpi.com/1424-8220/21/12/4204 |
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