Flex Force Smart Glove Prototype for Physical Therapy Rehabilitation

Abstract

A nonintrusive and noninvasive Flex Force Smart Glove (FFSG) design is presented that allows for acquisition and processing of sensorimotor information obtained from the human hand. The novel FFSG design is powered by the Intel FPGA system on chip and incorporates all the sensors needed to measure the force and rotation of the human wrist and fingers. Quaternion-based Kalman filters are used to fuse the raw sensor data from five finger joints and one wrist joint to provide detailed orientation information. In addition, feed forward neural network filters are used to classify possible hand exercises that can be further used facilitate rehabilitation through exercise sessions. The novel design will allow for a unified way to quantify the effectiveness of both conventional and robotic-assisted rehabilitation.