abstract

• 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%27s 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. © 2015 by ASME.
• 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. © 2015 by ASME.

authors

• Chavez-Romero R.
• Coronado L.E.
• Cárdenas Galindo Juan Antonio
• Maya Méndez Mauro Eduardo
• Piovesan D.

publication date

• 2015-01-01

published in

• ASME 2015 Dynamic Systems and Control Conference, DSCC 2015  Proceedings

keywords

• Adaptive control systems; Aerospace applications; Algorithms; Automobile engines; Bandpass filters; Battery management systems; Dynamics; Electric machine control; Electric power system control; Engines; Extended Kalman filters; Feedback control; Genetic algorithms; Gravitation; Hybrid vehicles; Intelligent robots; Intelligent systems; Internal combustion engines; Kalman filters; Muscle; Optimization; Parameter estimation; Wind power; Biomechanical parameters; Body's center of gravities; Estimation techniques; Experimental validations; Internal perturbation; Mechanical systems; Postural stability; State augmented extended kalman filters; Robotics

Digital Object Identifier (DOI)

• 10.1115/DSCC2015-9781

start page

end page

volume

• 1