Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses

This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips' contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R-2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R-2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

Automated summary

This paper presents the development of an intelligent soft-sensor system that adds haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed: one for finger joint angles and the other for fingertips' contact force. The sensors showed polynomial response with R-2 values higher than 92% for angle measurements and higher than 94% for tactile force measurements. The system successfully monitored the prosthesis activity and classified eight grip types with high accuracy using machine learning algorithms.