| 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 |
| Full Text |
PDF |
