The HMM-Based Sensing Correction Method for Leap Motion Finger Tracking

Abstract

Leap Motion is a fast and high accuracy optical sensor for finger tracking. However, it has limitation when fingers and palm lapped over each other. In this scenario, the front part sheltered rear part and makes the rear part unobservable to yield sensing error. This study proposes a novel method for error correction from the blind zone of Leap Motion. The proposed method focuses on data correction of finger direction by using Hidden Markov Model (HMM). Data captured from Leap Motion is a time series data set and the finger movement model behavior exactly similar to the characteristic of HMM (doubly stochastic process). Each of hidden states stands for a space of finger movement region. Through Viterbi algorithm, the exact region of the finger can be identified. The test data consists of finger-opened movement and finger-closed movement for each state, which create confusion matrix that indicated the recognition rate have significantly improved. All the ACC, TPR and FPR are better than directly observed from Leap Motion. The proposed method shows that HMM can alleviate the obstacle of the blind zone and increase the correct rate of finger tracking without much delay. That is helpful for the scenario of gesture control that needed much accuracy in the blind zone. One of the appropriate application is anthropomorphic robotic hand control. The proposed method is able to capture the finger movement correctly. For this reason, anthropomorphic robotic hand can be controlled well.