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Taking care of the elders constitutes a major issue in the western societies. Smart homes appear to be a socially and\udeconomically viable solution. They consist in habitats augmented with sensors and actuators enabling to achieve\udactivity recognition and to provide assistive services to a resident. Stationary aspect of sensors used in most smart\udhomes makes the concept difficult to deploy in existing homes, and involves a high cost. In this paper, we propose an inexpensive non-vision-based system ably to recognize, in real-time, activities and errors of a resident. This proposed recognition system is based on a shoe equipped with a single sensor: a three-axis accelerometer and on a statetransition algorithmic approach using fuzzy logic. We have examined the learning data as frequency distributions, where the probability histograms have been directly interpreted as fuzzy set. We conducted experiments\udof the system in our smart home by simulating (multiple times) several scenarios based on a morning routine. These\udscenarios were based on clinical data gathered in a previous experiment We obtained promising results showing that the proposed activity and error recognition system are highly effective

Towards a Real-time Error Detection within a Smart Home by Using Activity Recognition with a Shoe-mounted Accelerometer