Abstract
| Original language | English |
|---|---|
| Title of host publication | 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC) |
| Publisher | IEEE |
| Pages | 1528-1533 |
| ISBN (Electronic) | 978-1-5090-4372-9 |
| ISBN (Print) | 978-1-5090-4373-6 |
| Publication status | Published - 20 Jul 2017 |
| Event | International Wireless Communications and Mobile Computing Conference - Holiday Inn, Valencia, Spain Duration: 26 Jun 2017 → 30 Jun 2017 Conference number: 13 http://iwcmc.org/2017/ (Link to the conference website) |
Conference
| Conference | International Wireless Communications and Mobile Computing Conference |
|---|---|
| Abbreviated title | IWCMC2017 |
| Country | Spain |
| City | Valencia |
| Period | 26/06/17 → 30/06/17 |
| Internet address |
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Passive Wireless Sensing for Unsupervised Human Activity Recognition in Healthcare. / Li, Wenda; Tan, Bo; Xu, Yangdi; Piechocki, Robert.
2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2017. p. 1528-1533.Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding
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TY - GEN
T1 - Passive Wireless Sensing for Unsupervised Human Activity Recognition in Healthcare
AU - Li, Wenda
AU - Tan, Bo
AU - Xu, Yangdi
AU - Piechocki, Robert
PY - 2017/7/20
Y1 - 2017/7/20
N2 - Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.
AB - Physical activity classification is an important tool for various applications such as activity of daily living (ADL) recognition and fall detection. Additionally, the non-contact nature of radar systems provides minimally invasive sensing platform. Doppler-based radar has been used for activity classification in the past. However, most of these studies considered supervised classification which requires labeled training data sets. In this paper, we propose a novel procedure of using micro Doppler radar for unsupervised classification with Hidden Markov Models (HMM). A low-complexity time alignment method for capturing activity is developed and an Elbow test has been adopted for model selection. Test results confirm the efficacy of the selected feature set and the proposed methodology. The results prove the proposed system can deliver a very good performance in ADL recognition tasks.
UR - http://iwcmc.org/2017/
M3 - Conference proceeding
SN - 978-1-5090-4373-6
SP - 1528
EP - 1533
BT - 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)
PB - IEEE
ER -