The challenge of recognizing different personal activities while living in an apartment ½ is of great interest for the AAL community. Many different approaches have been presented trying ¾ to achieve good accuracies in activity recognition, combined with different heuristics, windowing ¿ and segmentation methods. In this paper we want to revisit the basic methodology proposed by a naive Bayes implementation with emphasis on multi-type event-driven location-aware activity recognition. Our method combines multiple events generated by binary sensors fixed to everyday objects, a capacitive smart floor, the received signal strength (RSS) from BLE beacons to a smart-watch and the sensed acceleration of the actor's wrist. Our new method does not use any segmentation phase, it interprets the received events as soon as they are measured and activity estimations are generated in real-time without any post-processing or time-reversal re-estimation. An activity prediction model is used in order to guess the more-likely next activity to occur. The
In REF , multiple events from binary sensors, a capacitive smart floor and a wearable beacon with an accelerometer are combined together in a realtime segmentation-free approach to predict 24 different personal activities in an apartment.