Improvement in Multi-resident Activity Recognition System in a Smart Home Using Activity Clustering

Human Activity Recognition (HAR) integrates ambient assisted living (AAL), leading to smart home automation for monitoring activities, healthcare, fall detection, etc. Various researchers have proposed a single-resident HAR system for ambient-sensor based smart home data, which is simple, and single-resident is not always the case. Multi-resident recognition is slightly complex and time-consuming. The researchers have made several efforts to generate benchmark datasets, such as CASAS, ARAS, vanKasteren, etc., for baseline comparison and performance analysis. However, these datasets have certain limitations, such as data association, annotation scarcity, computational cost, and even with data collection itself. This paper profoundly analyzed these limitations and manually clustered the activity labels to record the improvement in the performance of the system in terms of both recognition rate and computational time on the ARAS dataset.