Abstract
Activity recognition allows ubiquitous wearable device like smart watch to simplify the study and experiment. It is very convenient and extensibil-ity that we do study with the accelerometer sensor of a smart watch. In this paper, we use Samsung GEAR smart watch to collect data, then extract features, classify with H-SVM (Hierarchical Support Vector Machine) classifier and identify hu-man activities classification. Experiment results show great effect at low sam-pling rate, such as 10 Hz and 5 Hz, which will give us the energy saving. In most cases, the accuracies of activity recognition experiment are above 99%.
| Original language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Publisher | Association for Computing Machinery |
| Number of pages | 8 |
| Publication status | Accepted/In press - 8 Apr 2016 |
| Event | The 5th International Conference on Frontier Computing (FC 2016) - Duration: 8 Apr 2016 → … |
Conference
| Conference | The 5th International Conference on Frontier Computing (FC 2016) |
|---|---|
| Period | 8/04/16 → … |
Keywords
- Human Activity Recognition
- Smart Watch
- H-SVM
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Cite this
Tang, T., Zheng, Q., Weng, S., Peng, A., & Zheng, H. (Accepted/In press). Human Activity Recognition with Smart Watch based on H-SVM. In Unknown Host Publication Association for Computing Machinery. http://uir.ulster.ac.uk/35147/2/FC_2016_paper_35_acceptance.pdf