{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T20:52:17Z","timestamp":1775767937008,"version":"3.50.1"},"reference-count":64,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T00:00:00Z","timestamp":1490745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s17040713","type":"journal-article","created":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T11:26:44Z","timestamp":1490786804000},"page":"713","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Model-Based Estimation of Ankle Joint Stiffness"],"prefix":"10.3390","volume":"17","author":[{"given":"Berno","family":"Misgeld","sequence":"first","affiliation":[{"name":"Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstrasse 20, 52074 Aachen, Germany"}]},{"given":"Tony","family":"Zhang","sequence":"additional","affiliation":[{"name":"Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstrasse 20, 52074 Aachen, Germany"}]},{"given":"Markus","family":"L\u00fcken","sequence":"additional","affiliation":[{"name":"Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstrasse 20, 52074 Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6898-6887","authenticated-orcid":false,"given":"Steffen","family":"Leonhardt","sequence":"additional","affiliation":[{"name":"Philips Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstrasse 20, 52074 Aachen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/1743-0003-6-20","article-title":"Review of control strategies for robotic movement training after neurologic injury","volume":"6","author":"Reinkensmeyer","year":"2009","journal-title":"J. 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