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.
|Title of host publication||2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|Publication status||Published - 2017 Jul 25|
|Event||14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017 - Jeju, Korea, Republic of|
Duration: 2017 Jun 28 → 2017 Jul 1
|Name||2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017|
|Other||14th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2017|
|Country||Korea, Republic of|
|Period||17/6/28 → 17/7/1|
Bibliographical noteFunding 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.
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Biomedical Engineering
- Artificial Intelligence
- Human-Computer Interaction
- Control and Optimization