SMARE: Semi-supervised method for activities of daily living recognition

The significant growth of the average age of the world population requires effective solutions to meet the unsustainable number of requests for hospitalization. For this reason, the scientific community is seeking new paradigms of care that provide compensatory interventions to the first signs of warning that can be detected in the behavior of the individuals. At this aim, this work proposes a method to detect the Activities of Daily Living carried out by a subject monitored at home through unobtrusive environmental sensors. The Activity Recognition system is composed of an unsupervised segmentation layer which splits the collected sensors activation data in time periods and a semantic layer that exploits a knowledge-based approach to provide the most probable activity label for each period. The system is enriched with a further layer to adapt the base knowledge according to the information directly provided by the resident.