A Survey on Predicting Resident Intentions Using Contextual Modalities in Smart Home

Abstract

The Smart Home is an environment that enables the resident to interact with home appliances which provide resident intended services. In recent years, predicting resident intention based on the contextual modalities like activity, speech, emotion, object affordances, and physiological parameters have increased importance in the field of pervasive computing. Contextual modality is the feature through which resident interacts with the home appliances like TVs, lights, doors, fans, etc. These modalities assist the appliances in predicting the resident intentions making them recommend resident intended services like opening and closing doors, turning on and off televisions, lights, and fans. Resident-appliance interaction can be achieved by embedding artificial intelligence-based machine learning algorithms into the appliances. Recent research works on the contextual modalities and associated machine learning algorithms which are required to build resident intention prediction system have been surveyed in this article. A classification taxonomy of contextual modalities is also discussed.