Immediate Discrimination of Hand Movements Using Recurrent Neural Networks and Quantification of Cognitive Tasks Using Rock-Paper-Scissors Games

Abstract

Hand movement is an essential motor function in daily life, and maintaining the motor function of the hand is important to prevent a decline in quality of life. Hand exercises are effective in preventing dementia, promoting health, and improving motor functions, and are actively incorporated into rehabilitation. However, the accelerating aging of the population has led to an increase in the number of patients and a shortage of therapists, so we need a system that can a patient evaluates the motor function of his own by oneself. In this paper, we aim to realize a system that enables quantitative evaluation of hand movement functions by games using a motion sensor, Leapmotion, and a recu rrent neural network.