Survey on Temporal Patterns in Physiological Sensor Data

In past few years development of wearable sensors for health monitoring systems has garnered lots of attention in the scientific community and the industry. A mining system can predict the future health status of the patient based on the temporal trajectories of health status of number of similar patients. The wearable sensor technology increases the attention in both clinical and at home settings, the accumulation of physiological sensor data requires a concentrated effort on the analysis and modeling of this data. Main aspect of study in such system is how the data is treated and processed. Many paper overviews the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. This paper attempts to comprehensively review the current research and development on wearable sensor systems for health monitoring. And also latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. There is need of more efficient and correctly functioning method for mining data from wearable sensors.