An Adaptive Approach for Online Segmentation of Multi-Dimensional Mobile Data

With increasing availability of mobile sensing devices including smartphones, online mobile data segmentation becomes an important topic in reconstructing and understanding mobile data. Traditional approaches like online time series segmentation either use a fixed model or only apply an adaptive model on one dimensional data; it turns out that such methods are not very applicable to build online segmentation for multiple dimensional mobile sensor data (e.g., 3D accelerometer or 11 dimension features like ‘mean’, ‘vari- ance’, ‘covariance’, ‘magnitude’, etc). In this paper, we design an adaptive model for segment- ing real-time accelerometer data from smartphones, which is able to (a) dynamically select suitable dimensions to build a model, and (b) adaptively pick up a proper model. In addition to using the traditional residual-style regression errors to evaluate time series segmentation, we design a rich metric to evaluate mobile data segmentation results, including (1) traditional regression error, (2) Information Retrieval style measurements (i.e., precision, recall, F-measure), and (3) segmentation time delay.