Identification of isolated biomechanical parameters with a wireless body sensor network

The accurate, real-time estimation of biomechanical joint parameters bears a potential benefit for many applications. Examples include the assessment of training success in movement therapy, the use as a quantitative clinical scale for joint rigidity or the use in the derivation of control parameters for active, intelligent orthotic or prosthetic devices. Such a realtime assessment system should be as unobtrusive as possible, minimising instrumentation effort for the user or the clinical staff. Towards this goal we have build a body sensor network (BSN) that is able to measure surface electromyogram and 9-degrees of freedom inertial/magnetic data at high sample rates. The measured data is preprocessed and subsequently used in an Unscented Kalman Filter in a model-based approach employing the nonlinear dynamics of the human knee kinematics. The derivation of biomechanical joint parameters, in our case the knee stiffness, can then be readily obtained from the nonlinear model. To validate BSN measurements, we present a novel test-bench and its corresponding nonlinear model. The biomechanical parameter estimator is validated in pendulum like motions on the test-bench and in experiments where the test subject is undergoing co-activation of extensor and flexor muscles acting on the knee.