Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework
Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (C...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Cyborg and Bionic Systems |
| Online Access: | https://spj.science.org/doi/10.34133/cbsystems.0250 |
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| author | Mingxiang Luo Meng Yin Jinke Li Ying Li Worawarit Kobsiriphat Hongliu Yu Tiantian Xu Xinyu Wu Wujing Cao |
| author_facet | Mingxiang Luo Meng Yin Jinke Li Ying Li Worawarit Kobsiriphat Hongliu Yu Tiantian Xu Xinyu Wu Wujing Cao |
| author_sort | Mingxiang Luo |
| collection | DOAJ |
| description | Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the “Twin Brother” models. The “Twin Brother” model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model’s predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the R2 are 0.9853 ± 0.006 and 0.9808 ± 0.008. The accuracy of recognition and estimation are both better than comparative models. For gait phase percentage prediction, RMSE and R2 predicted by the model can reach 0.152° ± 0.014° and 0.986 ± 0.011, respectively. These results demonstrate that the method can be applied to lateral walking gait recognition and hip joint angle prediction. |
| format | Article |
| id | doaj-art-0da7c146f7c7423d8eb8a1326a74c8ba |
| institution | Kabale University |
| issn | 2692-7632 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Association for the Advancement of Science (AAAS) |
| record_format | Article |
| series | Cyborg and Bionic Systems |
| spelling | doaj-art-0da7c146f7c7423d8eb8a1326a74c8ba2025-08-20T03:51:59ZengAmerican Association for the Advancement of Science (AAAS)Cyborg and Bionic Systems2692-76322025-01-01610.34133/cbsystems.0250Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning FrameworkMingxiang Luo0Meng Yin1Jinke Li2Ying Li3Worawarit Kobsiriphat4Hongliu Yu5Tiantian Xu6Xinyu Wu7Wujing Cao8Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Department of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen, China.National Metal and Materials Technology Center, National Science and Technology Development Agency of Thailand, Khlong Nueng, Thailand.University of Shanghai for Science and Technology, Shanghai, China.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.Lateral walking exercise is beneficial for the hip abductor enhancement. Accurate gait recognition and continuous hip joint angle prediction are essential for the control of exoskeletons. We propose a dual-task learning framework, the “Twin Brother” model, which fuses convolutional neural network (CNN), long short-term memory (LSTM), neural networks (NNs), and the squeezing-elicited attention mechanism to classify the lateral gait stage and estimate the hip angle from electromyography (EMG) signals. The EMG signals of 6 muscles from 10 subjects during lateral walking were collected. Four gait phases were recognized, and the hip angles of both legs were continuously estimated. The sliding window length of 250 ms and the sliding increment of 3 ms were determined by the requirements of response time and recognition accuracy of the real-time system. We compared the performance of CNN-LSTM, CNN, LSTM, support vector machine, NN, K-nearest neighbor, and the “Twin Brother” models. The “Twin Brother” model achieved a recognition accuracy (mean ± SD) of 98.81% ± 0.14%. The model’s predicted root mean square error (RMSE) for the left and right hip angles are 0.9183° ± 0.024° and 1.0511° ± 0.027°, respectively, where the R2 are 0.9853 ± 0.006 and 0.9808 ± 0.008. The accuracy of recognition and estimation are both better than comparative models. For gait phase percentage prediction, RMSE and R2 predicted by the model can reach 0.152° ± 0.014° and 0.986 ± 0.011, respectively. These results demonstrate that the method can be applied to lateral walking gait recognition and hip joint angle prediction.https://spj.science.org/doi/10.34133/cbsystems.0250 |
| spellingShingle | Mingxiang Luo Meng Yin Jinke Li Ying Li Worawarit Kobsiriphat Hongliu Yu Tiantian Xu Xinyu Wu Wujing Cao Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework Cyborg and Bionic Systems |
| title | Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework |
| title_full | Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework |
| title_fullStr | Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework |
| title_full_unstemmed | Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework |
| title_short | Lateral Walking Gait Recognition and Hip Angle Prediction Using a Dual-Task Learning Framework |
| title_sort | lateral walking gait recognition and hip angle prediction using a dual task learning framework |
| url | https://spj.science.org/doi/10.34133/cbsystems.0250 |
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