Complex Activity Recognition Using Acceleration, Vital Sign, and Location Data

Abstract

Complex activity recognition is a valuable issue in mobile and wearable computing. Since complex activities are strongly relevant to users’ locations, location data can be used in complex activity recognition. Current activity recognition methods utilizing location data either do not make the most of location data or have a “cold-start” problem. In this paper, we propose CAROLINA (complex activity recognition using acceleration, vital sign, and location data) to recognize complex activities from wearable sensors. CAROLINA divides a map into grid cells and builds a grid cell-activity matrix by integrating check-in and ATUS (American Time Use Survey) datasets. Since some grid cells do not have check-in records, there are some null entries in the matrix, and all the entries are reconstructed by matrix factorization. Meanwhile, a POI (point of interest) category dataset is exploited to build a grid cell-grid cell similarity matrix, which is used to reduce the loss of matrix factorization. The results of experiments show that CAROLINA can effectively utilize location data to improve complex activity recognition performance.