Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917194371170304 |
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| author | Narasimhappa, Mundla Kumar, Praveen |
| author_facet | Narasimhappa, Mundla Kumar, Praveen |
| contents | Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM-UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and $R^2$ under subject-specific validation. The LSTM-UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6\% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4\% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06473 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation Narasimhappa, Mundla Kumar, Praveen Systems and Control Machine Learning Accurate prediction of joint kinematics and kinetics is essential for advancing gait analysis and developing intelligent assistive systems such as prosthetics and exoskeletons. This study presents a hybrid LSTM-UKF framework for estimating ankle angle and ground reaction force (GRF) across varying walking speeds. A multimodal sensor fusion strategy integrates force plate data, knee angle, and GRF signals to enrich biomechanical context. Model performance was evaluated using RMSE and $R^2$ under subject-specific validation. The LSTM-UKF consistently outperformed standalone LSTM and UKF models, achieving up to 18.6\% lower RMSE for GRF prediction at 3 km/h. Additionally, UKF integration improved robustness, reducing ankle angle RMSE by up to 22.4\% compared to UKF alone at 1 km/h. These results underscore the effectiveness of hybrid architectures for reliable gait prediction across subjects and walking conditions. |
| title | Hybrid LSTM-UKF Framework: Ankle Angle and Ground Reaction Force Estimation |
| topic | Systems and Control Machine Learning |
| url | https://arxiv.org/abs/2601.06473 |