IEEE transactions on bio-medical engineering · 2026
Fast Muscle-parameter Calibration using EMG and Markerless Kinematics for Neuromusculoskeletal Modeling: Application to Hand-cycling.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Ceglia Amedeo, Bailly Francois, Puchaud Pierre, Seoud Lama, Begon Mickael
Аннотация
OBJECTIVE: Estimating personalized muscle forces through musculoskeletal modeling is valuable for assessing patient status and monitoring clinical progress. However, this process involves numerous model parameters that are difficult to measure. Upper-limb applications are particularly limited due to the complexity of the system and the long computation times required for model calibration. This study proposes a rapid ($< $5 min) calibration method for upper-limb musculoskeletal models.
METHODS: We calibrated maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom by matching muscle-generated moments with dynamically consistent joint moments. The method leverages experimental data including bony landmark trajectories from markerless motion capture, external forces, and electromyography (EMG).
RESULTS: Joint moment estimation and calibration were completed together in less than five minutes. During hand-cycling, the calibrated model reduced EMG tracking error compared to the uncalibrated model (5.58$\pm$0.92% vs. 6.30$\pm$1.28%). Reliance on non-physiological residual moments was also lowered (12.68 vs. 23.61% of peak moment for calibrated vs. uncalibrated models, respectively).
CONCLUSION: The proposed method enables rapid calibration of upper-limb muscle parameters, improving accuracy in muscle force estimation and reducing dependence on residual moments.
SIGNIFICANCE: This approach provides a fast and reliable framework for upper-limb musculoskeletal calibration, facilitating more accurate and clinically applicable muscle force estimation.
METHODS: We calibrated maximal isometric force and optimal muscle length for 38 muscles across 10 degrees of freedom by matching muscle-generated moments with dynamically consistent joint moments. The method leverages experimental data including bony landmark trajectories from markerless motion capture, external forces, and electromyography (EMG).
RESULTS: Joint moment estimation and calibration were completed together in less than five minutes. During hand-cycling, the calibrated model reduced EMG tracking error compared to the uncalibrated model (5.58$\pm$0.92% vs. 6.30$\pm$1.28%). Reliance on non-physiological residual moments was also lowered (12.68 vs. 23.61% of peak moment for calibrated vs. uncalibrated models, respectively).
CONCLUSION: The proposed method enables rapid calibration of upper-limb muscle parameters, improving accuracy in muscle force estimation and reducing dependence on residual moments.
SIGNIFICANCE: This approach provides a fast and reliable framework for upper-limb musculoskeletal calibration, facilitating more accurate and clinically applicable muscle force estimation.