Resistance is Not Futile: Haptic Damping Forces Mitigate Effects of Motor Noise During Reaching
| Autor: | Niek Beckers, Herman van der Kooij, Arvid Q.L. Keemink |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Optimal estimation Computer science Movement (music) Feedback control 02 engineering and technology Optimal control Multiplicative noise 03 medical and health sciences 020901 industrial engineering & automation 0302 clinical medicine Control theory Motor noise Constant (mathematics) 030217 neurology & neurosurgery Haptic technology |
| Zdroj: | BioRob 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), 357-363 STARTPAGE=357;ENDPAGE=363;TITLE=2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob) |
| DOI: | 10.1109/biorob.2018.8488061 |
| Popis: | Understanding how users adapt their motor behavior to damping forces can improve assistive haptic shared control strategies, for instance in heavy robot-assisted lifting applications. In previous experiments we showed that subjects reaching in constant and position-dependent longitudinal damping fields were able to reduce their movement time and increase end-point accuracy. The movement time versus movement distance and prescribed end-point accuracy agreed with Fitts' Law. However, why subjects were able to have shorter movement time while subjected to impeding damping forces is not explained by Fitts' Law. Based on the minimal variance principle we propose that humans exploit the noise-filtering behavior of constant or position-dependent damping forces. These damping forces attenuate mechanical effects of activation-dependent motor noise. This allows for higher motor activation and shorter movement time without losing end-point accuracy. Consequently, higher allowed motor activation allows for higher accelerations that lead to higher peak velocities, resulting in shorter movement times. Linear and non-linear stochastic optimal feedback control and optimal estimation models with multiplicative noise corroborate measurement data, supporting our hypothesis. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|