Abstract
We address the estimation of biomechanical parameters with wearable measurement technologies. In particular, we focus on the estimation of sagittal plane ankle joint stiffness in dorsiflexion/plantar flexion. For this estimation, a novel nonlinear biomechanical model of the lower leg was formulated that is driven by electromyographic signals. The model incorporates a two-dimensional kinematic description in the sagittal plane for the calculation of muscle lever arms and torques. To reduce estimation errors due to model uncertainties, a filtering algorithm is necessary that employs segmental orientation sensor measurements. Because of the model’s inherent nonlinearities and nonsmooth dynamics, a square-root cubature Kalman filter was developed. The performance of the novel estimation approach was evaluated in silico and in an experimental procedure. The experimental study was conducted with body-worn sensors and a test-bench that was specifically designed to obtain reference angle and torque measurements for a single joint. Results show that the filter is able to reconstruct joint angle positions, velocities and torque, as well as, joint stiffness during experimental test bench movements.
Highlights
An accurate real-time estimation of human joint stiffness would be beneficial in many situations.For example, active prosthetics, orthotics, or exoskeletons could use information on quantitative joint stiffness to adjust their control strategies [1]
The filter of choice is cubature Kalman filter, since it increases numerical accuracy towards numerically problematic operations, such as the quadratic terms associated with the prediction of the covariance matrices [27]
A comparison between the cubature Kalman filter (CKF) and unscented Kalman filter (UKF) shows that the CKF is contained as a special case in the unscented transform [27]
Summary
Active prosthetics, orthotics, or exoskeletons could use information on quantitative joint stiffness to adjust their control strategies [1]. Such systems are increasingly fitted with physically adjustable elements [2]. The adjustment of, for example, a physically controllable interaction stiffness in robotic support is a possible area of application. Besides these movement support applications, time-resolved knowledge of human joint stiffness can be useful in motion analysis or as a detection system for pathologic movement states, such as spasticity [3].
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