Feature-Based Temporal Statistical Modeling of Data Streams from Multiple Wearable Devices
Tongtong Xu, Ao Guo, Jianhua Ma, Kevin I‐Kai Wang · 2017
Time is a vitally important issue in the coordination of multiple wearable devices. Theoretically, wearable applications should require data streams to be synchronized with the necessary degree of precision. However, in the available applications, this critical issue has not been well considered. Actually, time discrepancies exist among data streams, resulting in less accurate data analysis and fusion. The study of time discrepancy is rarely found in the literature, and there is no specific model to describe its features. In this paper, we first analyze the effect of time discrepancy on data. Then, by taking into account temporal features, we propose two typical models, which provide statistical methods for describing time discrepancy and its distribution. Furthermore, the accuracy of the models is verified by a set of experiments. Finally, we demonstrate the usability of the proposed models through a case study, in which the adaptive frequency strategy is adopted. Experimental results show that the strategy can not only guarantee the completeness of the data, but also reduce redundancy compared with the static frequency method. Our models and experiments of time discrepancy can be a basis for further research on the time synchronization of data from multiple wearable devices.