Model-Based Estimation of Ankle Joint Stiffness

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 4; p. 713
• Main Authors: Misgeld, Berno J E, Zhang, Tony, Lüken, Markus J, Leonhardt, Steffen
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2017; MDPI
• Subjects: Ankle Joint; Biomechanical Phenomena; Biomechanics; body-worn sensors; BSN; Computer Simulation; Filtration; Humans; joint stiffness estimation; Kalman filters; magnetic, angular rate and gravity sensors; Movement; Muscles; Parameter estimation; Stiffness; Torque; Two dimensional models; body-worn sensors; magnetic, angular rate and gravity sensors; joint stiffness estimation; BSN
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17040713

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.