This work proposes a set of simulation and experimental measurements to estimate muscle biomechanical parameter during human quiet standing. Understanding the mechanisms involved in postural stability is indispensable to improve the knowledge of how humans can regain balance against possible disturbances. Postural stability requires the ability to compensate the movement of the body’s center of gravity caused by external or internal perturbations. This paper describes the implementation of a hybrid parameter-estimation approach to infer the features of the human neuro-mechanical system during quiet standing and the recovery from a fall. The estimation techniques combines a genetic algorithm with the State-Augmented Extended Kalman Filter. These two algorithms running sequentially are utilized to estimate the musculo-skeletal parameters. This paper shows results of the approach when representing human standing as either a second-order or third order mechanical model. Experimental validation on a human subject is also presented.
- Dynamic Systems and Control Division
Combining Genetic Algorithms and Extended Kalman Filter to Estimate Ankle’s Muscle-Tendon Parameters
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Coronado, LE, Chavez-Romero, R, Maya, M, Cardenas, A, & Piovesan, D. "Combining Genetic Algorithms and Extended Kalman Filter to Estimate Ankle’s Muscle-Tendon Parameters." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems. Columbus, Ohio, USA. October 28–30, 2015. V001T15A002. ASME. https://doi.org/10.1115/DSCC2015-9781
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