A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains
Prediction of foot placement presents great potential in better assisting the walking of people with lower-limb disability in daily terrains. Previous researches mainly focus on foot placement prediction in level ground walking, however these methods cannot be applied to daily complex terrains inclu...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10319767/ |
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| author | Jingfeng Xiong Chuheng Chen Yuanwen Zhang Xinxing Chen Yuepeng Qian Yuquan Leng Chenglong Fu |
| author_facet | Jingfeng Xiong Chuheng Chen Yuanwen Zhang Xinxing Chen Yuepeng Qian Yuquan Leng Chenglong Fu |
| author_sort | Jingfeng Xiong |
| collection | DOAJ |
| description | Prediction of foot placement presents great potential in better assisting the walking of people with lower-limb disability in daily terrains. Previous researches mainly focus on foot placement prediction in level ground walking, however these methods cannot be applied to daily complex terrains including ramps, stairs, and level ground with obstacles. To predict foot placement in complex terrains, this paper presents a probability fusion approach for foot placement prediction in complex terrains which consists of two parts: model training and foot placement prediction. In the first part, a deep learning model is trained on augmented data to predict the probability distribution of preliminary foot placement. In the second part, environmental information and human walking constraints are used to calculate the feasible area, and finally the feasible area is fused with the probability distribution of preliminary foot placement to predict the foot placement in complex terrains. The proposed method can predict the foot placement of next step in complex terrains when heel-off is detected. Experiments (including structured terrains experiments and complex terrains experiments) show that the root mean square error (RMSE) of prediction is 8.19 ± 1.20 cm, which is less than 8% of the average stride length, and the landing feasible area accuracy (LFAA) of prediction is 95.11 ± 3.09%. Comparing with existing foot placement prediction studies, the method proposed in this paper achieves faster and more accurate prediction in complex terrains. |
| format | Article |
| id | doaj-art-867e5a0abbab4d1f86de5a946b38faeb |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-867e5a0abbab4d1f86de5a946b38faeb2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01314591460010.1109/TNSRE.2023.333368510319767A Probability Fusion Approach for Foot Placement Prediction in Complex TerrainsJingfeng Xiong0Chuheng Chen1https://orcid.org/0000-0002-5801-5307Yuanwen Zhang2Xinxing Chen3https://orcid.org/0000-0002-6265-1226Yuepeng Qian4https://orcid.org/0000-0001-5298-1849Yuquan Leng5https://orcid.org/0000-0003-4063-4545Chenglong Fu6https://orcid.org/0000-0002-8955-5429Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaShenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities and the Department of Mechanical and Energy Engineering, Southern University of Science and Technology, Shenzhen, ChinaPrediction of foot placement presents great potential in better assisting the walking of people with lower-limb disability in daily terrains. Previous researches mainly focus on foot placement prediction in level ground walking, however these methods cannot be applied to daily complex terrains including ramps, stairs, and level ground with obstacles. To predict foot placement in complex terrains, this paper presents a probability fusion approach for foot placement prediction in complex terrains which consists of two parts: model training and foot placement prediction. In the first part, a deep learning model is trained on augmented data to predict the probability distribution of preliminary foot placement. In the second part, environmental information and human walking constraints are used to calculate the feasible area, and finally the feasible area is fused with the probability distribution of preliminary foot placement to predict the foot placement in complex terrains. The proposed method can predict the foot placement of next step in complex terrains when heel-off is detected. Experiments (including structured terrains experiments and complex terrains experiments) show that the root mean square error (RMSE) of prediction is 8.19 ± 1.20 cm, which is less than 8% of the average stride length, and the landing feasible area accuracy (LFAA) of prediction is 95.11 ± 3.09%. Comparing with existing foot placement prediction studies, the method proposed in this paper achieves faster and more accurate prediction in complex terrains.https://ieeexplore.ieee.org/document/10319767/Foot placement predictionintention recognitionlower-limb exoskeletonssupervised learningsensor fusion |
| spellingShingle | Jingfeng Xiong Chuheng Chen Yuanwen Zhang Xinxing Chen Yuepeng Qian Yuquan Leng Chenglong Fu A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains IEEE Transactions on Neural Systems and Rehabilitation Engineering Foot placement prediction intention recognition lower-limb exoskeletons supervised learning sensor fusion |
| title | A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains |
| title_full | A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains |
| title_fullStr | A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains |
| title_full_unstemmed | A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains |
| title_short | A Probability Fusion Approach for Foot Placement Prediction in Complex Terrains |
| title_sort | probability fusion approach for foot placement prediction in complex terrains |
| topic | Foot placement prediction intention recognition lower-limb exoskeletons supervised learning sensor fusion |
| url | https://ieeexplore.ieee.org/document/10319767/ |
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