Touch-and-slippage detection algorithm for prosthetic hands. (October 2020)

• Record Type: Journal Article
• Authors: Gentile, Cosimo; Cordella, Francesca; Rodrigues, Cesar Ramos; Zollo, Loredana
• Is Part Of: Mechatronics. Volume 70(2020)
• Publication Date: 2020-10
• Subjects: Slippage detection -- Touch detection -- Force sensing resistor sensor -- Normal force component -- Prosthetics; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2020.102402
• Languages: English
• ISSNs: 0957-4158

Abstract

Abstract: The greatest limitation for an amputee subject who uses a prosthesis having no sensory feedback is the difficulty to manage unexpected events in an autonomous way. In grasp and manipulation tasks, the possibility of object slippage is high. For this reason, it is necessary to detect the beginning of the slippage and provide the control with a fast contrast action. In this work, a touch-and-slippage detection algorithm for effective grasp control of a prosthetic hand embedding monoaxial, low-cost sensors is proposed. The algorithm has a low computational burden, and is capable of detecting slippage events using only the normal force component. Three experimental sessions were performed to validate the touch-and-slippage algorithm. In the first one, the variation of the normal force component provided by a Force Sensing Resistor (FSR) sensor was compared with the tangential force component measured by a triaxial force sensor. In the second one, the threshold for detecting slippage events was obtained and applied to study the dependence of the number of slippage events on sliding velocity and force applied on the sensor. Finally, the algorithm was tested on a real system made of the IH2 Azzurra robotic hand provided with FSR on the fingertips. The obtained results are illustrated in terms of false positives and negatives and true positives and negatives. The low computation time of the algorithm, i.e. 4.9 ms, and the high success rate in terms of detected slippage events (99.4%) suggest that the algorithm applicability in real systems is feasible.