Robotic solutions to facilitate studying human motor control

Ahmed Ramadan, Jongeun Choi, Clark J. Radcliffe, Jacek Cholewicki, N. Peter Reeves, John M. Popovich

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Studying human motor control requires innovative engineering solutions including robots. For example, the motor control task(s) must be reliable before using system identification (SYSID) tools to estimate any motor control descriptors, e.g. neurophysiological parameters. Robots can improve the experiment reliability in contrast to passive devices, e.g. seated balance on a hemisphere. First, we present some novel physical human-robot interaction (pHRI) tasks in the biomechanics field. Then, we show how one pHRI can achieve excellent reliability measures. Moreover, robots can allow the use a wide variety of input/perturbation signals that may be designed specifically for better SYSID results, such as better estimation error variance. With standard signals, however, further analysis is required to improve the SYSID outcomes. Therefore, we devised an analysis method based on Fisher information to reduce the estimation error variance.

Original languageEnglish
Title of host publication2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages174-178
Number of pages5
ISBN (Electronic)9781509030552
DOIs
Publication statusPublished - 2017 Jul 25
Event14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 - Jeju, Korea, Republic of
Duration: 2017 Jun 282017 Jul 1

Publication series

Name2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017

Other

Other14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017
Country/TerritoryKorea, Republic of
CityJeju
Period17/6/2817/7/1

Bibliographical note

Funding Information:
This publication was made possible in part by grant number U19 AT006057 from the National Center for Complementary and Integrative Health (NCCIH) at the National Institutes of Health. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NCCIH.

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Biomedical Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Optimization

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