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Construction of a Dataset for Static and Dynamic Hand Tracking Using a Non-invasive Environment

Abstract:

The research consists of hand gesture recognition using a commercial sensor called Leap Motion Controller. For the study, we determined hand tracking carried out for 5 static and 4 dynamic gestures. For this work, the stages of the gesture recognition study were divided into data acquisition, preprocessing, feature extraction, and classification. For the data acquisition stage, we programmed an interface in MATLAB, which generates a structure of output data in images and spatial position forms. The preprocessing consists of taking the data acquired by the leap motion controller and transform them into data readable by MATLAB using the MEX compiler, these data are captured during the execution of the gesture. Besides, the proposed data acquisition interface collect demographic data, hand injuries, and the light intensity of the environment in which the data is taken. These data can be used in hand gesture recognition systems and application fields such as medicine, engineering, and robotics, among others. The development of the acquisition system is parameterized with 30 repetitions at 60 frames per second and the sampling time at 5 s. Finally, the system previews a graph of the gesture recognition test structure in specific activity time.