Gaze, visual, myoelectric, and inertial data of grasps for intelligent prosthetics

Abstract

A hand amputation is a highly disabling event, having severe physical and psychological repercussions on a person’s life. Despite extensive efforts devoted to restoring the missing functionality via dexterous myoelectric hand prostheses, natural and robust control usable in everyday life is still challenging. Novel techniques have been proposed to overcome the current limitations, among them the fusion of surface electromyography with other sources of contextual information. We present a dataset to investigate the inclusion of eye tracking and first person video to provide more stable intent recognition for prosthetic control. This multimodal dataset contains surface electromyography and accelerometry of the forearm, and gaze, first person video, and inertial measurements of the head recorded from 15 transradial amputees and 30 able-bodied subjects performing grasping tasks. Besides the intended application for upper-limb prosthetics, we also foresee uses for this dataset to study eye-hand coordination in the context of psychophysics, neuroscience, and assistive robotics.

Measurement(s)	muscle electrophysiology trait • eye movement measurement • first person video • body movement coordination trait • head movement trait • eye-hand coordination
Technology Type(s)	electromyography • eye tracking device • Accelerometer • accelerometer and gyroscope • data transformation
Factor Type(s)	age • sex • handedness • amputation side • amputation cause • years since amputation • residual limb length • prosthesis
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11672442