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

Measurement(s)muscle electrophysiology trait center dot eye movement measurement center dot first person video center dot body movement coordination trait center dot head movement trait center dot eye-hand coordinationTechnology Type(s)electromyography center dot eye tracking device center dot Accelerometer center dot accelerometer and gyroscope center dot data transformationFactor Type(s)age center dot sex center dot handedness center dot amputation side center dot amputation cause center dot years since amputation center dot residual limb length center dot prosthesisSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11672442 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.