Intervention of non-inhabitant activities detection in smart home environment

Abstract

Inhabitants daily activity form a pattern in their daily life which has important things in smart home. These patterns can be used to recognize the inhabitant activity that is useful to enhance the smart home services like energy efficiency service, where these patterns can be used as inhabitant behavior to reduce an unnecessary appliances or lightings usage based on the activities their conduct. Recognition accuracy is important things for providing particular service needs on automation process in smart home, but activity recognition faces many challenges in real world cause of diversity and complexity of the activities. Inter-subject variability activities often appear in real world situation that accuracy of recognition process can be affected. For instance, there is a possible situation where family or colleague visits to inhabitant's home in long term. Non-inhabitant activities may conduct with a different way or different behavior than inhabitant does. This situation is producing activities where is not carried from legitimate inhabitant. In this paper, we propose a method to overcome the activity recognition issue that commonly occurred. Our proposed method using temporal relation approach, which can detect a non-inhabitant activity. This approach is separating detected activities from inhabitant's observed activities, so the activity recognition will perform effectively. We assess the effectiveness of our approach using Activity Daily Living (ADL) provided by WSU Smart Home Project dataset.