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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.07527 |
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| _version_ | 1866910000328212480 |
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| author | Wu, Kaida Xiang, Peihao Lin, Chaohao Bai, Ou |
| author_facet | Wu, Kaida Xiang, Peihao Lin, Chaohao Bai, Ou |
| contents | To enhance lifting-load estimation accuracy in industrial upper-limb assistive exoskeletons, this study proposes a machine learning-based approach using insole pressure sensors. Unlike traditional methods that rely on electromyography (EMG), force sensors, or posture data, insole pressure sensors provide a non-invasive, posture-independent, and stable solution suitable for long-term use. Lifting load data ranging from 2 to 10 kg (0.5 kg intervals) were collected from five subjects. Two data representations were investigated: channel-based vectors and map-based images. For the channel-based approach, conventional regression models (SVR, MLP, and Elastic Net) were trained on pooled data from all subjects to assess inter-subject generalization, specifically testing the ability to infer load levels unseen during training. In parallel, a preliminary feasibility study was conducted for the map-based deep learning model (MobileNetV2) using inner-subject data. Results indicate that the channel-based SVR achieved the most balanced accuracy and generalization performance, with a mean absolute error of 0.547 kg. These findings demonstrate the feasibility and advantages of using insole pressure data for variable load estimation, supporting control strategies in industrial exoskeleton applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_07527 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning Wu, Kaida Xiang, Peihao Lin, Chaohao Bai, Ou Systems and Control To enhance lifting-load estimation accuracy in industrial upper-limb assistive exoskeletons, this study proposes a machine learning-based approach using insole pressure sensors. Unlike traditional methods that rely on electromyography (EMG), force sensors, or posture data, insole pressure sensors provide a non-invasive, posture-independent, and stable solution suitable for long-term use. Lifting load data ranging from 2 to 10 kg (0.5 kg intervals) were collected from five subjects. Two data representations were investigated: channel-based vectors and map-based images. For the channel-based approach, conventional regression models (SVR, MLP, and Elastic Net) were trained on pooled data from all subjects to assess inter-subject generalization, specifically testing the ability to infer load levels unseen during training. In parallel, a preliminary feasibility study was conducted for the map-based deep learning model (MobileNetV2) using inner-subject data. Results indicate that the channel-based SVR achieved the most balanced accuracy and generalization performance, with a mean absolute error of 0.547 kg. These findings demonstrate the feasibility and advantages of using insole pressure data for variable load estimation, supporting control strategies in industrial exoskeleton applications. |
| title | Real-Time Load Estimation for Load-lifting Exoskeletons Using Insole Pressure Sensors and Machine Learning |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.07527 |