Semi-supervised Discovery of Time-series Templates for Gesture Spotting in Activity Recognition

Héctor F. Satizábal, Julien Rebetez, Andres Perez-Uribe

2013

Abstract

In human activity recognition, gesture spotting can be achieved by comparing the data from on-body sensors with a set of known gesture templates. This work presents a semi-supervised approach to template discovery in which the Dynamic Time Warping distance measure has been embedded in a classic clustering technique. Clustering is used to find a set of template candidates in an unsupervised manner, which are then evaluated by means of a supervised assessment of their classification performance. A cross-validation test over a benchmark dataset showed that our approach yields good results with the advantage of using a single sensor.

References

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Paper Citation


in Harvard Style

F. Satizábal H., Rebetez J. and Perez-Uribe A. (2013). Semi-supervised Discovery of Time-series Templates for Gesture Spotting in Activity Recognition . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 573-576. DOI: 10.5220/0004268805730576


in Bibtex Style

@conference{icpram13,
author={Héctor F. Satizábal and Julien Rebetez and Andres Perez-Uribe},
title={Semi-supervised Discovery of Time-series Templates for Gesture Spotting in Activity Recognition},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={573-576},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004268805730576},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Semi-supervised Discovery of Time-series Templates for Gesture Spotting in Activity Recognition
SN - 978-989-8565-41-9
AU - F. Satizábal H.
AU - Rebetez J.
AU - Perez-Uribe A.
PY - 2013
SP - 573
EP - 576
DO - 10.5220/0004268805730576