Adam D Goodworth1, Robert J Peterka2. 1. University of Hartford, 200 Bloomfield Avenue West Hartford, CT 06117, United States. Electronic address: goodworth@hartford.edu. 2. Oregon Health & Science University 3181 SW Sam Jackson Park Road, Portland, OR 97239, United States; National Center for Rehabilitative Auditory Research, VA Portland Health Care System, 3710 SW US Veterans Hospital Road, Portland, OR 97239, United States.
Abstract
BACKGROUND: Posture control models are instrumental to interpret experimental data and test hypotheses. However, as models have increased in complexity to include multi-segmental dynamics, discrepancy has arisen amongst researchers regarding the accuracy and limitations of identifying neural control parameters using a single stimulus. NEW METHOD: The current study examines this topic using simulations with a parameterized model-fit approach. We first determine if the model-fit approach can identify parameters in the theoretical situation with no noise. Then, we measure variability and bias of parameter estimates when realistic noise is included. We also address how the accuracy is influenced by the frequency bandwidth of the stimulus, signal-to-noise of the data, and fitting procedures. RESULTS: We found perfect identification of parameters in the theoretical model without noise. With realistic noise, bias errors were 4.4% and 7.6% for fits that included frequencies 0.02-1.2 Hz and 0.02-0.4 Hz, respectively. Fits between 0.02-1.2 Hz also had the lowest variability in parameter estimates compared to other bandwidths. Parameters with the lowest variability tended to have the largest influence on body sways. Results also demonstrated the importance of closely examining model fits because of limitations in fitting algorithms. COMPARISON WITH EXISTING METHOD: The single-input model-fit approach may be a simpler and more practical method for identifying neural control mechanisms compared to a multi-stimulus alternative. CONCLUSIONS: This study provides timely theoretical and practical considerations applicable to the design and analysis of experiments contributing to the identification of mechanisms underlying stance control of a multi-segment body.
BACKGROUND: Posture control models are instrumental to interpret experimental data and test hypotheses. However, as models have increased in complexity to include multi-segmental dynamics, discrepancy has arisen amongst researchers regarding the accuracy and limitations of identifying neural control parameters using a single stimulus. NEW METHOD: The current study examines this topic using simulations with a parameterized model-fit approach. We first determine if the model-fit approach can identify parameters in the theoretical situation with no noise. Then, we measure variability and bias of parameter estimates when realistic noise is included. We also address how the accuracy is influenced by the frequency bandwidth of the stimulus, signal-to-noise of the data, and fitting procedures. RESULTS: We found perfect identification of parameters in the theoretical model without noise. With realistic noise, bias errors were 4.4% and 7.6% for fits that included frequencies 0.02-1.2 Hz and 0.02-0.4 Hz, respectively. Fits between 0.02-1.2 Hz also had the lowest variability in parameter estimates compared to other bandwidths. Parameters with the lowest variability tended to have the largest influence on body sways. Results also demonstrated the importance of closely examining model fits because of limitations in fitting algorithms. COMPARISON WITH EXISTING METHOD: The single-input model-fit approach may be a simpler and more practical method for identifying neural control mechanisms compared to a multi-stimulus alternative. CONCLUSIONS: This study provides timely theoretical and practical considerations applicable to the design and analysis of experiments contributing to the identification of mechanisms underlying stance control of a multi-segment body.
Authors: Denise Engelhart; Alfred C Schouten; Ronald G K M Aarts; Herman van der Kooij Journal: IEEE Trans Neural Syst Rehabil Eng Date: 2014-11-20 Impact factor: 3.802