Real-time human behaviour monitoring using hybrid ambient assisted living framework

The primary objective of the ambient assisted living (AAL) is to enhance the living standards of the aged person by presenting various assistive solutions. The services presented to the occupants in AAL are localization, context awareness, activity and behaviour monitoring, personalization, overcoming loneliness, privacy, security, and many more. To endeavour the user with these services, human-activity recognition and human behaviour modeling is a fundamental necessity. Many researchers have assumed only the periodic and consistent behaviour of the users while designing their frameworks; in a real scenario, human behaviour is unpredictable. There arises a need for converting the proposal into the working prototype due to the enormous difference between the available solutions and actual needs. This paper introduces a novel hybrid framework for human behaviour modeling in AAL, which accommodates the varying nature of activity and behaviour of the inhabitant. The proposal uses feedback-based mechanism to recursively append new events and behaviour, such that they are classified into normal or abnormal human behaviour. The proposed solution uses the pattern mining and clustering mechanism to determine the anomalies in addition to the camera sensor, which detects human fall to deal with any critical situation. Once the action is recognized, it is more convenient to extend essential services like medical assistance or emergency response.