A multivariate symbolic approach to activity recognition for wearable applications

With the aim of monitoring human activities (in critical tasks as well as in leisure and sport activities), wearable devices provide enhanced usability and seamless human experience with respect to other portable devices (e.g. smartphones). At the same time, though, wearable devices are more resource-constrained in terms of computational capability and memory, which calls for the design of algorithmic solutions that explicitly take into account these issues. In this paper, a symbolic approach for activity recognition with wearable devices is presented: the Symbolic Aggregate approXimation technique is here extended to multi-dimensional time series, in order to capture the mutual information of different dimensions. Moreover, a novel approach to identify gestures within activities is here presented. The performance of the proposed methodology is tested on the two heterogeneous datasets related to cross-country skiing and daily activities. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.