An experimental study on new features for activity of daily living recognition

In the last few years, the researchers have spent many efforts in developing advanced systems for activity daily living (ADL) recognition in diverse applicative contexts, as home automation and ambient assisted living. Some of these need to know in real time the actions performed by a user, and this involves a number of additional issues to be taken into account during the recognition. In this paper, we present some improvements of a sliding window based approach to perform ADL recognition in a online fashion, i.e., recognizing activities as and when new sensor events are recorded. We describe seven methods used to extract features from the sequence of sensor events. The first four relate to previous works regarding the system of ADL recognition described, while, the last three represent the original contribution of this work. Support Vector Machine (SVM) has been used as classifier. Several experiments have been carried out by using a public smart home dataset and obtained results show that two of the three novel approaches allow to improve the recognition performance of the conventional methods, up to an increment of 5% with respect to the baseline feature extraction approach.