Article: Optimal design of motor learning experiments informed by Monte-Carlo simulation Journal: International Journal of Experimental Design and Process Optimisation (IJEDPO) 2021 Vol.6 No.4 pp.289 - 303 Abstract: In our field of neuro-rehabilitation, it is often difficult to have patients endure a long session of training, and we seek an estimate of minimum practice trials. In such cases, motor learning measurements across trials are usually exponentially decaying transient signals. Here we employed Monte-Carlo methods to determine the minimum number of samples required from transient responses. We tracked the accuracy of recovery of synthesised data to reveal a prescription for the minimum number of samples for a robust identification of the underlying learning process, given preliminary estimates of the time constant and noise levels. Our results revealed a systematic relationship for the minimum number of samples required from transient signals that can be used as a stopping criteria for data collection. We also evaluated these results by using a past motor learning study and determining the minimum number of required samples (trials) to best estimate learning curves. Inderscience Publishers - linking academia, business and industry through research

Title: Optimal design of motor learning experiments informed by Monte-Carlo simulation

Authors: Pritesh N. Parmar; James L. Patton

Addresses: Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, 851 South Morgan Street, Chicago, Illinois, 60607, USA; Shirley Ryan AbilityLab (Formerly RIC), 355 East Erie Street, Chicago, Illinois, 60611, USA ' Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, 851 South Morgan Street, Chicago, Illinois, 60607, USA; Shirley Ryan AbilityLab (Formerly RIC), 355 East Erie Street, Chicago, Illinois, 60611, USA

Abstract: In our field of neuro-rehabilitation, it is often difficult to have patients endure a long session of training, and we seek an estimate of minimum practice trials. In such cases, motor learning measurements across trials are usually exponentially decaying transient signals. Here we employed Monte-Carlo methods to determine the minimum number of samples required from transient responses. We tracked the accuracy of recovery of synthesised data to reveal a prescription for the minimum number of samples for a robust identification of the underlying learning process, given preliminary estimates of the time constant and noise levels. Our results revealed a systematic relationship for the minimum number of samples required from transient signals that can be used as a stopping criteria for data collection. We also evaluated these results by using a past motor learning study and determining the minimum number of required samples (trials) to best estimate learning curves.

Keywords: optimal design; motor learning; exponential regression; first-order system; Monte-Carlo method; point of diminishing returns; sample size; signal-to-noise ratio; time constant; transient response.

DOI: 10.1504/IJEDPO.2021.117248

International Journal of Experimental Design and Process Optimisation, 2021 Vol.6 No.4, pp.289 - 303

Received: 07 May 2021
Accepted: 12 Jul 2021
Published online: 24 Aug 2021 *

Title: Optimal design of motor learning experiments informed by Monte-Carlo simulation

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