Computationally fast estimation of muscle tension for realtime bio-feedback

In this paper, we propose a method for realtime estimation of whole-body muscle tensions. The main problem of muscle tension estimation is that there are infinite number of solutions to realize a particular joint torque due to the actuation redundancy. Numerical optimization techniques, e.g. quadratic programming, are often employed to obtain a unique solution, but they are usually computationally expensive. For example, our implementation of quadratic programming takes about 0.17 sec per frame on the musculoskeletal model with 274 elements, which is far from realtime computation. Here, we propose to reduce the computational cost by using EMG data and by reducing the number of unknowns in the optimization. First, we compute the tensions of muscles with surface EMG data based on a biological muscle data, which is a very efficient process. We also assume that their synergists have the same activity levels and compute their tensions with the same model. Tensions of the remaining muscles are then computed using quadratic programming, but the number of unknowns is significantly reduced by assuming that the muscles in the same heteronymous group have the same activity level. The proposed method realizes realtime estimation and visualization of the whole-body muscle tensions that can be applied to sports training and rehabilitation.