Activity recognition using correlated pattern mining for people with dementia

Abstract

Due to the rapidly aging population around the world, senile dementia is growing into a prominent problem in many societies. To monitor the elderly dementia patients so as to assist them in carrying out their basic Activities of Daily Living (ADLs) independently, sensors are deployed in their homes. The sensors generate a stream of context information, i.e., snippets of the patient's current happenings, and pattern mining techniques can be applied to recognize the patient's activities based on these micro contexts. Most mining techniques aim to discover frequent patterns that correspond to certain activities. However, frequent patterns can be poor representations of activities. In this paper, instead of using frequent patterns, we propose using correlated patterns to represent activities. Using simulation data collected in a smart home testbed, our experimental results show that using correlated patterns rather than frequent ones improves the recognition performance by 35.5% on average.