We propose a novel approach for detecting events in data sequences, based on a predictive method using Gaussian processes. We have applied this approach for detecting relevant events in the therapeutic exercise sequences, wherein obtained results in addition to a suitable classifier, can be used directly for gesture segmentation. During exercise performing, motion data in the sense of 3D position of characteristic skeleton joints for each frame are acquired using a RGBD camera. Trajectories of joints relevant for the upper-body therapeutic exercises of Parkinson’s patients are modelled as Gaussian processes. Our event detection procedure using an adaptive Gaussian process predictor has been shown to outperform a first derivative based approach.
CITATION STYLE
Spasojevic, S., Ventura, R., Santos-Victor, J., Potkonjak, V., & Rodić, A. (2016). Automatic segmentation of therapeutic exercises motion data with a predictive event approach. In Mechanisms and Machine Science (Vol. 38, pp. 217–225). Kluwer Academic Publishers. https://doi.org/10.1007/978-3-319-23832-6_18
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