A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance
Lower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force...
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/11115116/ |
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| author | Delaney E. Miller Ashley E. Brown Nicholas A. Bianco Scott L. Delp Steven H. Collins |
| author_facet | Delaney E. Miller Ashley E. Brown Nicholas A. Bianco Scott L. Delp Steven H. Collins |
| author_sort | Delaney E. Miller |
| collection | DOAJ |
| description | Lower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force-sensing knee implants and complexity of simulation-based methods. We developed two data-driven models to separately estimate the peaks in knee contact force during early and late stance using a limited set of features from electromyography (EMG), ground reaction force (GRF), and knee angle recordings. These models were trained on experimental data from healthy young adults (N = 6) walking with a wide range of knee-ankle exoskeleton torque assistance conditions. Peak knee contact forces were obtained from EMG-informed musculoskeletal simulations in OpenSim Moco. The data-driven models were evaluated using leave-one-subject-out cross validation on their ability to accurately compare exoskeleton assistance conditions. The data-driven models identified directional changes in peak knee contact force larger than 0.1 body weights (BW) with 90% accuracy for early-stance peak and 79% accuracy for late-stance peak. Both models included GRF and knee angle features, but EMG features reflected phase-specific muscle activity: quadriceps appeared in the early-stance model, plantar flexors in late stance, and hamstrings in both. We developed a simple method to rapidly estimate changes in peak knee contact force. This approach is suitable for systematic interventions that aim to reduce knee load, such as human-in-the-loop optimization of exoskeleton assistance. |
| format | Article |
| id | doaj-art-1376fa71b672467bbc8d5e28aa8b6b92 |
| institution | Kabale University |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-1376fa71b672467bbc8d5e28aa8b6b922025-08-20T03:41:19ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01333116312810.1109/TNSRE.2025.359626111115116A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton AssistanceDelaney E. Miller0https://orcid.org/0000-0003-1704-845XAshley E. Brown1Nicholas A. Bianco2Scott L. Delp3https://orcid.org/0000-0001-6184-2728Steven H. Collins4https://orcid.org/0000-0002-3997-3374Department of Mechanical Engineering, Stanford University, Stanford, CA, USADepartment of Mechanical Engineering, Stanford University, Stanford, CA, USADepartment of Mechanical Engineering, Stanford University, Stanford, CA, USADepartment of Mechanical Engineering, and the Department of Bioengineering and Orthopaedic Surgery, Stanford University, Stanford, CA, USADepartment of Mechanical Engineering, Stanford University, Stanford, CA, USALower-limb exoskeletons could benefit individuals with knee osteoarthritis by reducing knee loading. Real-time estimation of knee loads could accelerate the development of load-reducing exoskeletons. However, measuring or estimating internal knee forces remains challenging due to the rarity of force-sensing knee implants and complexity of simulation-based methods. We developed two data-driven models to separately estimate the peaks in knee contact force during early and late stance using a limited set of features from electromyography (EMG), ground reaction force (GRF), and knee angle recordings. These models were trained on experimental data from healthy young adults (N = 6) walking with a wide range of knee-ankle exoskeleton torque assistance conditions. Peak knee contact forces were obtained from EMG-informed musculoskeletal simulations in OpenSim Moco. The data-driven models were evaluated using leave-one-subject-out cross validation on their ability to accurately compare exoskeleton assistance conditions. The data-driven models identified directional changes in peak knee contact force larger than 0.1 body weights (BW) with 90% accuracy for early-stance peak and 79% accuracy for late-stance peak. Both models included GRF and knee angle features, but EMG features reflected phase-specific muscle activity: quadriceps appeared in the early-stance model, plantar flexors in late stance, and hamstrings in both. We developed a simple method to rapidly estimate changes in peak knee contact force. This approach is suitable for systematic interventions that aim to reduce knee load, such as human-in-the-loop optimization of exoskeleton assistance.https://ieeexplore.ieee.org/document/11115116/Exoskeletonjoint loadknee contact forcemusculoskeletal simulationosteoarthritis |
| spellingShingle | Delaney E. Miller Ashley E. Brown Nicholas A. Bianco Scott L. Delp Steven H. Collins A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance IEEE Transactions on Neural Systems and Rehabilitation Engineering Exoskeleton joint load knee contact force musculoskeletal simulation osteoarthritis |
| title | A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance |
| title_full | A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance |
| title_fullStr | A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance |
| title_full_unstemmed | A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance |
| title_short | A Data-Driven Approach to Estimate Changes in Peak Knee Contact Force With Exoskeleton Assistance |
| title_sort | data driven approach to estimate changes in peak knee contact force with exoskeleton assistance |
| topic | Exoskeleton joint load knee contact force musculoskeletal simulation osteoarthritis |
| url | https://ieeexplore.ieee.org/document/11115116/ |
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