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Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control

Liu, Quanying and Nakahira, Yorie and Mohideen, Ahkeel and Dai, Adam and Choi, Sunghoon and Pan, Angelina and Ho, Dimitar M. and Doyle, John C. (2019) Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control. In: 2019 American Control Conference (ACC). IEEE , Piscataway, NJ, pp. 483-488. ISBN 978-1-5386-7926-5. https://resolver.caltech.edu/CaltechAUTHORS:20190905-143550126

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Abstract

This paper describes several surprisingly rich but simple demos and a new experimental platform for human sensorimotor control research and also controls education. The platform safely simulates a canonical sensorimotor task of riding a mountain bike down a steep, twisting, bumpy trail using a standard display and inexpensive off-the-shelf gaming steering wheel with a force feedback motor. We use the platform to verify our theory, presented in a companion paper. The theory tells how component hardware speed-accuracy tradeoffs (SATs) in control loops impose corresponding SATs at the system level and how effective architectures mitigate the deleterious impact of hardware SATs through layering and “diversity sweet spots” (DSSs). Specifically, we measure the impacts on system performance of delays, quantization, and uncertainties in sensorimotor control loops, both within the subject's nervous system and added externally via software in the platform. This provides a remarkably rich test of the theory, which is consistent with all preliminary data. Moreover, as the theory predicted, subjects effectively multiplex specific higher layer planning/tracking of the trail using vision with lower layer rejection of unseen bump disturbances using reflexes. In contrast, humans multitask badly on tasks that do not naturally distribute across layers (e.g. texting and driving). The platform is cheap to build and easy to program for both research and education purposes, yet verifies our theory, which is aimed at closing a crucial gap between neurophysiology and sensorimotor control. The platform can be downloaded at https://github.com/Doyle-Lab/WheelCon.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://ieeexplore.ieee.org/document/8814470PublisherArticle
ORCID:
AuthorORCID
Liu, Quanying0000-0002-2501-7656
Nakahira, Yorie0000-0003-3324-4602
Doyle, John C.0000-0002-1828-2486
Additional Information:© 2019 AACC.
Record Number:CaltechAUTHORS:20190905-143550126
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190905-143550126
Official Citation:Q. Liu et al., "Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control," 2019 American Control Conference (ACC), Philadelphia, PA, USA, 2019, pp. 483-488. URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8814470&isnumber=8814292
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98440
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:05 Sep 2019 22:37
Last Modified:03 Oct 2019 21:41

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