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Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation

Menrva Research Group, School of Mechatronic Systems and Engineering Science, Simon Fraser University, Metro Vancouver, BC V3T 0A3, Canada
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Sensors 2020, 20(7), 2104; https://doi.org/10.3390/s20072104
Received: 29 January 2020 / Revised: 25 March 2020 / Accepted: 3 April 2020 / Published: 8 April 2020
(This article belongs to the Section Biomedical Sensors)
Force myography (FMG) signals can read volumetric changes of muscle movements, while a human participant interacts with the environment. For collaborative activities, FMG signals could potentially provide a viable solution to controlling manipulators. In this paper, a novel method to interact with a two-degree-of-freedom (DoF) system consisting of two perpendicular linear stages using FMG is investigated. The method consists in estimating exerted hand forces in dynamic arm motions of a participant using FMG signals to provide velocity commands to the biaxial stage during interactions. Five different arm motion patterns with increasing complexities, i.e., “x-direction”, “y-direction”, “diagonal”, “square”, and “diamond”, were considered as human intentions to manipulate the stage within its planar workspace. FMG-based force estimation was implemented and evaluated with a support vector regressor (SVR) and a kernel ridge regressor (KRR). Real-time assessments, where 10 healthy participants were asked to interact with the biaxial stage by exerted hand forces in the five intended arm motions mentioned above, were conducted. Both the SVR and the KRR obtained higher estimation accuracies of 90–94% during interactions with simple arm motions (x-direction and y-direction), while for complex arm motions (diagonal, square, and diamond) the notable accuracies of 82–89% supported the viability of the FMG-based interactive control. View Full-Text
Keywords: force myography signal; exerted hand force; intended arm motion; biaxial stage; planar workspace; collaborative interactions; machine learning force myography signal; exerted hand force; intended arm motion; biaxial stage; planar workspace; collaborative interactions; machine learning
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Zakia, U.; Menon, C. Estimating Exerted Hand Force via Force Myography to Interact with a Biaxial Stage in Real-Time by Learning Human Intentions: A Preliminary Investigation. Sensors 2020, 20, 2104.

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