Sparse natural gesture spotting in free living to monitor drinking with wrist-worn inertial sensors
Giovanni Schiboni, Oliver Amft · 2018
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.