Unsupervised gesture segmentation of a real-time data stream in MATLAB
Miguel A. Simao, Pedro Neto, Olivier Gibaru · 2016
Continuous and real-time gesture spotting is a key factor in the development of novel human-robot interaction (HRI) modalities. Gesture recognition can be greatly improved with previous reliable segmentation. This paper introduces a new unsupervised threshold-based gesture segmentation method to accurately divide continuous data streams into dynamic and static blocks, without previous knowledge of gesture data (unbounded input data). This type of segmentation may reduce the number of wrongly classified gestures. The proposed approach identifies sudden inversions of movement direction which are a cause of over segmentation. This is achieved by the analysis of velocities and accelerations numerically derived from positional data. A genetic optimization algorithm is used to optimize thresholds from calibration data. Experimental tests were based on the application of the proposed method using a data glove and a magnetic tracking device as interaction technology. The over-segmentation error was 5.5% in a benchmark for motion segmentation composed of samples retrieved from two subjects.