Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| 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 → … |
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Keywords
- Human Activity Recognition
- Smart Watch
- H-SVM
Cite this
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Human Activity Recognition with Smart Watch based on H-SVM. / Tang, Tao; Zheng, Qingxiang; Weng, Shaolin; Peng, Ao; Zheng, Huiru.
Unknown Host Publication. 2016.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Human Activity Recognition with Smart Watch based on H-SVM
AU - Tang, Tao
AU - Zheng, Qingxiang
AU - Weng, Shaolin
AU - Peng, Ao
AU - Zheng, Huiru
PY - 2016/4/8
Y1 - 2016/4/8
N2 - 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%.
AB - 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%.
KW - Human Activity Recognition
KW - Smart Watch
KW - H-SVM
M3 - Conference contribution
BT - Unknown Host Publication
ER -