Characterizing and Compensating for Errors in a Leap Motion using PCA

This paper concerns a rehabilitation framework that makes use of a low cost "off-the-shelf" device. The device is a visual markerless sensor system called the Leap Motion controller (LM). However, before deploying the LM, we investigate its accuracy and limitations in measuring finger joint angles. During a rehabilitation procedure, patients will be flexing and extending their fingers and accurate feedback is a prerequisite for them to benefit effectively from the exercises. During finger joint angle error analysis, we conducted a series of experiments to assess the accuracy of the LM in terms of parameters like elevation, lateral (side-to-side) positioning, forward-backward positioning, and rotation of the hand relative to the LM. We used an "artist’s hand" placed above the LM. The artist’s hand is more accurate than a human hand in performing static hand gestures as it can maintain a fixed posture as long as is necessary. According to the results of the error analysis, we apply Principal Component Analysis (PCA) to the LM raw data to see whether the algorithm can compensate for these errors. The experimental results show that the PCA algorithm is feasible, effective and can be applied such that fairly accurate measurements can be obtained and therefore suitable feedback can be provided to the patient using the LM for hand rehabilitation purposes.