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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.
|Title of host publication||2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob)|
|Publisher||IEEE Computer Society|
|Number of pages||7|
|Publication status||Published - 9 Oct 2018|
|Event||7th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2018|
: High Tech Human Touch - University Campus, Enschede, Netherlands
Duration: 26 Aug 2018 → 29 Aug 2018
Conference number: 7
|Conference||7th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics, BioRob 2018|
|Period||26/08/18 → 29/08/18|
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