Name
Title | ||
|---|---|---|
Cross-location transfer learning for the sussex-huawei locomotion recognition challenge |
Abstract | ||
|---|---|---|
The Sussex-Huawei Locomotion Challenge 2019 was an open competition in activity recognition where the participants were tasked with recognizing eight different modes of locomotion and transportation. The main difficulty of the challenge is that the training data was recorded with a smartphone that was placed in a different body location than the test data. Only a small validation set with all locations was provided to enable transfer learning. This paper describes our (team JSI First) approach, in which we first derived additional sensor streams from the existing ones and on them calculated a large body of features. We then used cross-location transfer learning via specialized feature selection, and performed two-step classification. Finally, we used Hidden Markov Models to alter the predictions in order to take their temporal dependencies into account. Internal tests using this methodology yielded an accuracy of 83%.
|
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3344856 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
activity recognition, competition, feature extraction, machine learning, smartphone, transfer learning | Computer vision,Computer science,Transfer of learning,Human–computer interaction,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Janko | 1 | 15 | 8.34 |
| Martin Gjoreski | 2 | 32 | 8.08 |
| Carlo Maria De Masi | 3 | 0 | 0.34 |
| Nina Resçiç | 4 | 3 | 2.82 |
| Mitja Luštrek | 5 | 410 | 54.52 |
| Matjaz Gams | 6 | 536 | 80.90 |