HANDdata – first-person dataset including proximity and kinematics measurements from reach-to-grasp actions

HANDdata is a dataset designed to provide hand kinematics and proximity vision data during reach to grasp actions of non-virtual objects, specifically tailored for autonomous grasping of a robotic hand, and with particular attention to the reaching phase. We sought to capture details of the reach-to-grasp action by looking at target object characteristics from proximity, at wrist and fingers kinematics, and at hand-object interaction main events. We structured the data collection as a sequence of static and dynamic tasks, organized by increasing levels of complexity. HANDdata is a first-person, reach-to-grasp dataset that includes almost 6000 human-object interactions from 29 healthy adults, with 10 standardized objects of 5 different shapes and 2 kinds of materials. We believe that such data collection can be of value for researchers interested in autonomous grasping robots for healthcare and industrial applications, as well as for those interested in radar-based computer vision and in basic aspects of sensorimotor control and manipulation.