Name
Abstract | ||
|---|---|---|
Wearable health device refers to the using of wearable sensors to collect and monitor biological and physiological parameters of human motion streaming data. Generally it contains heart rate, pulse rate, respiratory rate, body temperature, heat consumption, blood pressure, blood sugar, blood oxygen, hormones and BMI index, body fat content, and etc. It helps users to manage their physiological activities. The objective of this research is to develop an anomaly detection algorithm for data collected from medical wireless sensors. We fist introduce our framework, then focus on representing historical anomalies and the matching algorithms to detect potential anomalies. The experimental results on real patient datasets show that the proposed approach can efficiently detect patients' anomalies and sense fault with high accuracy meanwhile keeping reasonable alarm precision and recall ratios. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.55 | 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech) |
Keywords | Field | DocType |
anomaly detection,streaming data,wearable health devices,sensor management | Anomaly detection,Wireless,Wearable computer,Computer science,ALARM,Precision and recall,Real-time computing,Streaming data,Fist,Wireless sensor network | Conference |
ISBN | Citations | PageRank |
978-1-5386-1957-5 | 0 | 0.34 |
References | Authors | |
3 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peipei Wang | 1 | 6 | 4.81 |
| Yutong Han | 2 | 0 | 0.34 |
| Jing Qin | 3 | 0 | 0.34 |
| Bin Wang | 4 | 42 | 7.78 |
| Xiaochun Yang | 5 | 440 | 52.12 |