Abstract
Control of reach-to-grasp movements for deft and robust interactions with objects requires rapid sensorimotor updating that enables online adjustments to changing external goals (e.g., perturbations or instability of objects we interact with). Rarely do we appreciate the remarkable coordination in reach-to-grasp, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Modeling online control of human reach-to-grasp movements is a challenging problem but fundamental to several domains, including behavioral and computational neuroscience, neurorehabilitation, neural prostheses, and robotics. Currently, there are no publicly available datasets that include online adjustment of reach-to-grasp movements to object perturbations. This work aims to advance modeling efforts of reach-to-grasp movements by making publicly available a large kinematic and EMG dataset of online adjustment of reach-to-grasp movements to instantaneous perturbations of object size and distance performed in immersive haptic-free virtual environment (hf-VE). The presented dataset is composed of a large number of perturbation types (10 for both object size and distance) applied at three different latencies after the start of the movement.
Highlights
Background & SummaryRarely do we appreciate the remarkable coordination involved in our routine reach-to-grasp movements, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging
Do we appreciate the remarkable coordination involved in our routine reach-to-grasp movements, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging
To advance our understanding of how reach-to-grasp movements are orchestrated and updated, the scientific community needs to turn to more sophisticated forms of characterizing this complex motor behavior
Summary
Background & SummaryRarely do we appreciate the remarkable coordination involved in our routine reach-to-grasp movements, until control becomes impaired by neurological injuries such as stroke, neurodegenerative diseases, or even aging. Large publicly available datasets of hand movements of grasping 3D objects[22,23,24,25,26] recorded using video/Kinect/infrared motion capture have immensely benefited modeling efforts in grasp classification. These datasets are often collected with the explicit purpose of training robotic grasping and are not optimized for modeling of human manual behavior. There is no publicly available dataset that includes online adjustment of reach-to-grasp movements to perturbations, freely accessible to researchers from multiple fields for modeling and analytical means. A reach-to-grasp dataset that offers synchronized kinematic and electromyography (EMG) data for a broader set of conditions, including coordinated reach and grasp responses to perturbations of the task goal, would greatly aid future efforts directed toward modeling of reach-to-grasp movements
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