Explorations of Autonomous Prosthetic Grasping via Proximity Vision and Deep Learning

Abstract

The traumatic loss of a hand is usually followed by significant psychological, functional and rehabilitation challenges. Even though much progress has been reached in the past decades, the prosthetic challenge of restoring the human hand functionality is still far from being achieved. Autonomous prosthetic hands showed promising results and wide potential benefit, a benefit that must be still explored and deployed. Here, we hypothesized that a combination of a radar sensor and a low-resolution time-of-flight camera can be sufficient for object recognition in both static and dynamic scenarios. To test this hypothesis, we analyzed via deep learning algorithms HANDdata, a human-object interaction dataset with particular focus on reach-to-grasp actions. Inference testing was also performed on unseen data purposely acquired. The analyses reported here, broken down to gradually increasing levels of complexity, showed a great potential of using such proximity sensors as alternative or complementary solution to standard camera-based systems. In particular, integrated and low-power radar can be a potential key technology for next generation intelligent and autonomous prostheses.