Abstract
We present and validate a computationally efficient lower limb musculoskeletal model for the control of a rehabilitation robot. It is a parametric model that allows the customization of joint kinematics, and it is able to operate in real time. Methods: Since the rehabilitation exercises corresponds to low-speed movements, a quasi-static model can be assumed, and then muscle force coefficients are position dependent. This enables their calculation in an offline stage. In addition, the concept of a single functional degree of freedom is used to minimize drastically the workspace of the stored coefficients. Finally, we have developed a force calculation process based on Lagrange multipliers that provides a closed-form solution; in this way, the problem of dynamic indeterminacy is solved without the need to use an iterative process. Results: The model has been validated by comparing muscle forces estimated by the model with the corresponding electromyography (EMG) values using squat exercise, in which the Spearman’s correlation coefficient is higher than 0.93. Its computational time is lower than 2.5 ms in a conventional computer using MATLAB. Conclusions: This procedure presents a good agreement with the experimental values of the forces, and it can be integrated into real time control systems.
Highlights
Lower limb musculoskeletal models (MSM) constitute a powerful simulation tool with numerous applications in the field of sports and rehabilitation
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MSMs used for controlling RPRs must be adaptable to the patient’s characteristics, simple, and able to operate in real time, while providing accurate estimates of the muscle forces and identifying overexertion in the body structures
Summary
Lower limb musculoskeletal models (MSM) constitute a powerful simulation tool with numerous applications in the field of sports and rehabilitation. Despite the progress made in lower limb MSM, they still have some drawbacks which affect their validity [1] and their usefulness in clinical applications [2] These drawbacks are related with three fundamental aspects: (i) have a precise and adjustable kinematic model for each patient; (ii) keep the model complexity as low as possible; and (iii) experimentally validate the model. The most widely used models represent the knee joint using a mobile axis of rotation, whose position and orientation are related to knee flexion angle. This relationship is obtained experimentally in in vitro studies, which limits its realism and customization capacity [7], mainly when applied to people with medical conditions [8,9]
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