Publications

Sparse Natural Gesture Spotting in Free Living to Monitor Drinking with Wrist-worn Inertial Sensors

Publication Type Conference Paper
Authors Giovanni Schiboni, Oliver Amft
Title Sparse Natural Gesture Spotting in Free Living to Monitor Drinking with Wrist-worn Inertial Sensors
Abstract We present a spotting network composed of Gaussian Mixture Hidden Markov Models (GMM-HMMs) to detect sparse natural gestures in free living. The key technical features of our approach are (1) a method to mine non-gesture patterns that deals with the arbitrary data (Null Class), and (2) an optimisation based on multipopulation genetic programming to approximate spotting network's parameters across target and non-target models. We evaluate our GMM-HMMs spotting network in a novel free living dataset, including totally 35 days of annotated inertial sensor's recordings from seven participants. Drinking was chosen as target gesture. Our method reached an average F1-score of over 74% and clearly outperformed an HMM-based threshold model approach. The results suggest that our spotting network approach is viable for sparse natural pattern spotting.
Date 2018
Proceedings Title Proceedings of the 2018 ACM International Symposium on Wearable Computers
Conference Name 22nd International Symposium on Wearable Computers (ISWC '18)
Place New York, NY, USA
Publisher ACM
Pages 140–147
Series ISWC '18
DOI 10.1145/3267242.3267253
ISBN 978-1-4503-5967-2
URL Publisher's website
Library Catalog ACM Digital Library
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Friedrich-Alexander-Universität Erlangen-Nürnberg