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access icon free Upper body estimation of muscle forces, muscle states, and joint motion using an extended Kalman filter

© The Institution of Engineering and Technology
Received 10/03/2020, Accepted 09/11/2020, Revised 04/10/2020, Published 25/11/2020

This study synthesises modelling techniques and dynamic state estimation techniques for the simultaneous estimation of the muscle states, muscle forces, and joint motion states of a dynamic human arm model. The estimator considers both muscle dynamics and motion dynamics. The arm model has two joints and six muscles and contains dynamics both of the muscles and of the motion. We develop an optimally tuned extended Kalman filter using noisy measurements of joint angles with standard deviation 2.87, of joint velocities with standard deviation 6.9/s, and of muscle activations with standard deviation 10% of their peak values, and then simultaneously estimate joint angles, joint velocities, muscle forces, joint moments, and muscle states. The standard deviations of estimation errors (SDEE) are no more than 0.07° for joint angles, 1/s for joint velocities, 0.6 mm for muscle–tendon lengths, and 0.1 Nm for joint torques. The results are compared with a previously developed static optimisation method, and verify the effectiveness of the proposed estimator in providing lower SDEE for both muscle and motion dynamics of the human arm model compared to the static optimisation method.

Inspec keywords: medical signal processing; state estimation; electromyography; Kalman filters; biomechanics; nonlinear filters

Other keywords: muscle dynamics; joint torques; joint motion states; electromyography; simultaneous estimation; estimation errors; optimally tuned extended Kalman filter; dynamic state estimation techniques; upper body estimation; joint moments; joint angles; muscle states; muscle activations; motion dynamics; standard deviation; muscle-tendon lengths; dynamic human arm model; muscle forces; joint velocities

Subjects: Electrical activity in neurophysiological processes; Electrodiagnostics and other electrical measurement techniques; Physics of body movements; Digital signal processing; Filtering methods in signal processing; Bioelectric signals; Biology and medical computing

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