Research And Application Of Wearable Device Data Analysis Technology

With the rapid development and the widespread popularity of wearable devices, the amount of data about moving, sleeping and blood glucose generated by wearable devices is bigger and bigger. And the development of the data mining technology also lay a good basis for the further development of the wearable devices. Only combined with the technology of data mining, can the intelligent wearable devices provide more valuable information for the users, which can further improve the users’stickiness. How to make full use of these data to produce more valuable information is attracting more and more researchers’attention.Based on the analysis of time series of periodic pattern mining technology development status, aiming at the limitation in detecting cross-nesting cycle model based on periodic pattern mining algorithm, this paper improved the algorithm. Also, according to the feature of lung function measurements PEF, FEV1, this paper proposed the corresponding method of data preprocessing, which not only reduces the size of original dataset but also retains the local characteristics of time series. In order to help asthma patients regularly measure PEF/FEV1 to track the development of the disease, a wearable peak flow meter APP was developed, which is convenient to collect PEF/FEV1 data anywhere, anytime. At last, a peak flow meter data analysis system was developed based on Software Engineer by integrating all the technology mentioned above.