A Feasibility Analysis of a Finger Motion Capture System using Kinect
Date Issued
2012
Date
2012
Author(s)
Chen, Wei-Chih
Abstract
The system architecture of this thesis utilized concept modify from Microsoft Kinect sensor. We utilized depth information generated by Light Coding Technology in the Kinect to capture spatial coordinates of fingertip in real world space. Then, we assess the feasibility of using Kinect sensor on finger motion capture in real world. OpenNI is used to retrieve the needed information. In fingertip detection, we used k-curvature algorithm to find out the fingertip location. Validation of the Kinect space coordinates for Z-axis depth information, X-axis and Y-axis length distance were also done. In addition, the analysis of stability on fingertip detection algorithm was also presented. The mean absolute percentage error (MAPE) and mean squared error (MSE) are evaluation tools. This help to find out the maximum bending angle of sample finger.
In the verification experiment on the real space coordinates of the Kinect, we defined the depth measurement distance is from 50 cm to 130 cm. We found that the error value is proportional to the depth distance, but the average error rates are less than 1%. In the validation of horizontal and vertical distance, the error rate can be controlled within the acceptable rage (less than 5%) on the certain depth distance (80-110cm). In the stability analysis of fingertip detection algorithm, the MAPE value in tri-axial detection of each fingertip is mostly below 10. In general, average coordinates distance error is proportional to the depth distance within the measurement distance. Finger maximum bending angle that can be detected is about 30-45 degree, which is limited by the optical limitation.
According to the result in this experiment, it is feasible to use the Kinect as finger capture system. Although limited by the hardware, the subjects’ position of hand and the rage of activities must be strictly limited. Nevertheless, for the patient with certain degree of recovery, using the Kinect within appropriate training mode can enable them to self-intensive training to live with interesting. Patient do not go to hospital can also improve hand function. In addition, clinical physician can currently assess the patients’ condition by this system, and the clinical physician can also modify the training program as needed.
Subjects
Kinect sensor
Open Natural Interaction (OpenNI)
Open Source Computer Vision (OpenCV)
Fingertip detection
Mean absolute percentage error (MAPE)
Mean squared error (MSE)
Type
thesis
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