Improving Robotic Hand Prosthesis Control With Eye Tracking and Computer Vision: A Multimodal Approach Based on the Visuomotor Behavior of Grasping - Cognolato, Matteo; Atzori, Manfredo; Gassert, Roger; Müller, Henning (Frontiers in Artificial Intelligence, 2022)
The complexity and dexterity of the human hand make the development of natural and robust 4 control of hand prostheses challenging. Although a large number of control approaches were 5 developed and investigated in the last decades, limited robustness in real-life conditions prevented 6 their application in clinical settings and in commercial products. In this paper, we investigate a 7 multimodal approach that exploits the use of eye-hand coordination to improve the control of 8 myoelectric hand prostheses. The analyzed data are from the recently released MeganePro 9 Dataset 1, that includes multimodal data from transradial amputees and able-bodied subjects 10 while grasping numerous household objects with ten grasp types. A continuous grasp-type 11 classification based on surface electromyography served as both intent detector and classifier. 12 At the same time, the information provided by eye-hand coordination parameters, gaze data 13 and object recognition in first-person videos allowed to identify the object a person aims to 14 grasp. The results show that the inclusion of visual information significantly increases the 15 average offline classification accuracy by up to 15.61 ± 4.22 % for the transradial amputees 16 and of up to 7.37 ± 3.52 % for the able-bodied subjects, allowing trans-radial amputees to reach 17 average classification accuracy comparable (and in some cases higher) than intact subjects and 18 suggesting that the robustness of hand prosthesis control based on grasp-type recognition can 19 be significantly improved with the inclusion of visual information extracted by leveraging natural 20 eye-hand coordination behavior and without placing additional cognitive burden on the user