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Dataset on Force Myography for Human–Robot Interactions

Author

Listed:
  • Umme Zakia

    (Menrva Research Group, School of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada)

  • Carlo Menon

    (Menrva Research Group, School of Mechatronic Systems Engineering and Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada
    Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Lengghalde 5, 8008 Zurich, Switzerland)

Abstract

Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.

Suggested Citation

  • Umme Zakia & Carlo Menon, 2022. "Dataset on Force Myography for Human–Robot Interactions," Data, MDPI, vol. 7(11), pages 1-12, November.
    • Handle: RePEc:gam:jdataj:v:7:y:2022:i:11:p:154-:d:966443
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