A parsimonious approach for activity recognition with wearable devices: An application to cross-country skiing

With the aim of monitoring the human activity, wearable devices provide an enhanced usability and a seamless human experience with respect to other portable devices (e.g. smartphones) in critical tasks as well as in leisure and sport activities. 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 parsimonious approach for activity recognition with wearable devices is presented. The methodology is based on Relevant Vector Machines (RVMs), a sparse machine learning framework for classification, and allows to tackle the activity recognition problem by identifying the two phases of Event Identification and Gesture Recognition. The performance of the presented methodology is tested on the interesting case study of cross-country skiing (classic style): such a dataset presents three different classes of gestures in addition to non-gesture activities and has been obtained by recording the training sessions of a heterogeneous set of executors in different environment conditions.