Proximity Perception-Based Grasping Intelligence: Toward the Seamless Control of a Dexterous Prosthetic Hand

• Published in: IEEE/ASME transactions on mechatronics Vol. 29; no. 3; pp. 2079 - 2090
• Main Authors: Heo, Si-Hwan, Park, Hyung-Soon
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autonomous system; Decision making; Electromyography; Feature extraction; Fingers; Grasping; Hand (anatomy); Intelligence; intention recognition; Perception; Point cloud compression; Prostheses; Prosthetic hand; Proximity; proximity perception; Real time; Real-time systems; Robot sensing systems; shared autonomy
• ISSN: 1083-4435; 1941-014X
• DOI: 10.1109/TMECH.2023.3324051

Achieving the dexterity of a human hand is a major goal in the field of prosthetic hands. To achieve this level of dexterity, a compact high degrees-of-freedom robotic prosthesis and seamless control of the hardware is needed. In this article, to attain seamless control of a highly dexterous prosthetic hand, we propose a novel perception system that provides grasping intelligence to the prosthetic hand. The proximity perception-based grasping intelligence (P2GI) system comprises a proximity sensor system and a prompt decision-making process. The proximity sensors embedded in the prosthetic hand map the point cloud of the object in real time while the prosthetic hand reaches toward the object. Simultaneously, a real-time decision-making algorithm infers the user's intended grasp posture by obtaining the hand-object relation from the point cloud data. The finger motion that stably grasps the target object with the inferred grasp posture is planned accordingly. Consequently, the user can intuitively utilize various grasp postures of the prosthetic hand using a single-channel surface electromyography signal. The P2GI system was evaluated with ten subjects. The results show a grasp posture classification accuracy of 97.8% and a task success rate of 95.7% during the real-time grasping for unknown objects in daily life.