Author:
Takagi Atsushi,De Magistris Giovanni,Xiong Geyun,Micaelli Alain,Kambara Hiroyuki,Koike Yasuharu,Savin Jonathan,Marsot Jacques,Burdet Etienne
Abstract
AbstractHumans have the ability to use a diverse range of handheld tools. Owing to its versatility, a virtual environment with haptic feedback of the force is ideally suited to investigating motor learning during tool use. However, few simulators exist to recreate the dynamic interactions during real tool use, and no study has compared the correlates of motor learning between a real and virtual tooling task. To this end, we compared two groups of participants who either learned to insert a real or virtual tool into a fixture. The trial duration, the movement speed, the force impulse after insertion and the endpoint stiffness magnitude decreased as a function of trials, but they changed at comparable rates in both environments. A ballistic insertion strategy observed in both environments suggests some interdependence when controlling motion and controlling interaction, contradicting a prominent theory of these two control modalities being independent of one another. Our results suggest that the brain learns real and virtual insertion in a comparable manner, thereby supporting the use of a virtual tooling task with haptic feedback to investigate motor learning during tool use.
Funder
Precursory Research for Embryonic Science and Technology
Japan Society for the Promotion of Science
European Commission
Horizon 2020 Framework Programme
Engineering and Physical Sciences Research Council
Publisher
Springer Science and Business Media LLC
Reference28 articles.
1. Conard, N. J. A female figurine from the basal Aurignacian of Hohle Fels Cave in southwestern Germany. Nature 459, 248–252 (2009).
2. Gaudez, C. Upper limb musculo-skeletal disorders and insert fitting activity in automobile sector: Impact on muscular stresses of fitting method and insert position on part. Comput. Methods Biomech. Biomed. Engin. 11, 101–102 (2008).
3. Kim, B., Park, J., Park, S. & Kang, S. Impedance learning for robotic contact tasks using natural actor-critic algorithm. IEEE Trans. Syst. Man Cybern. Part B Cybern. 40, 433–443 (2010).
4. Shadmehr, R. & Wise, S. P. The Computational Neurobiology of Reaching and Pointing: A Foundation for Motor Learning (MIT Press, Cambridge, 2005).
5. Venkadesan, M. & Valero-Cuevas, F. J. Neural control of motion-to-force transitions with the fingertip. J. Neurosci. 28, 1366–1373 (2008).
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献