A drift detecting anti-divergent EKF for online biodynamic model identification
This paper describes a novel online biodynamic model parameter estimation algorithm applied to data acquired from a small Inertial Measurement Unit network. The predicted biodynamic model represents the response motion of a human seated in a vehicle subjected to mechanical vibrations. The motivations for identifying the body model parameters are, to aid vibration isolation technologies and to continuously monitor the transmission of vibrations through the seated occupant. The algorithm consists of a bank of parallel Extended Kalman Filters for estimating each biodynamic parameter. During the posterior state prediction update step of the Extended Kalman Filter, an Adaptive Sliding Window concept drift detection algorithm maintains a variable window of past priori estimation errors. The model process error statistics are then updated based upon the statistics of the priori state errors in the window. The algorithm updates the estimator parameters incrementally and is suitable for streaming data. The estimator is applicable for cases where the distribution of the biodynamic model process noise is unknown or is abruptly varying, and when the model parameters are unknown. This estimator was evaluated experimentally with data sourced from a network of wearable wireless Inertial Measurement Units affixed to the test subject. The test subjects were then exposed to an external vibrating excitation source. The results validate that this algorithm provides accurate online estimates of the human biodynamic model parameters for a second order transmissibility model. The algorithm presented mitigates problems associated with the estimation of biodynamic parameters such as biomechanical nonlinearities, process noise drift and divergence.
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