Session 4: DHM in Biomechanics (Part 1)
Authors: Ritwik Rakshit (Texas Tech University) , Shuvrodeb Barman (Oklahoma State University) , Yujiang Xiang (Oklahoma State University) , James Yang (Texas Tech University)
The ability to predict the decline in muscle strength over the course of an activity (i.e., fatigue) can be a crucial aid to task design, injury prevention, and rehabilitation efforts. Current models of muscle fatigue have been hitherto validated only for isometric contractions, but most real-world tasks are dynamic in nature, involving continuously varying joint velocities. It has previously been proposed that a three-compartment-controller (3CC) model might be used to predict fatigue for such tasks by using it in conjunction with joint- and direction-specific torque-velocity-angle (TVA) surfaces. This allows for the calculation of a time-varying target load parameter that can be used by the 3CC model, but it increases model complexity and has not been validated by experimental data. An alternative approach is proposed where the effect of joint velocity is modeled by a velocity parameter and integrated into the fatigue model equations, removing the dependence on external TVA surfaces. The predictions using both methods are contrasted against experimental data collected from 20 subjects in a series of isokinetic tests involving the knee and shoulder joints, covering a range of velocities encountered in day-to-day tasks. A much lower degree of fatigue is observed for moderate velocities compared to that for very low or very high velocities. Predictions using the integrated velocity parameter are computationally less expensive than using TVA surfaces and are also closer to experimentally obtained values. The modified fatigue model can therefore be applied to dynamic tasks with varying velocities when the task is discretized into several isokinetic tasks.
Keywords: muscle fatigue, joint velocity, dynamic task
How to Cite: Rakshit, R. , Barman, S. , Xiang, Y. & Yang, J. (2022) “Joint velocity dependence of fatigue in isokinetic tasks”, Proceedings of the 7th International Digital Human Modeling Symposium. 7(1). doi: https://doi.org/10.17077/dhm.31764