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Activity recognition in smart home environment using wireless ambient sensing is a well-known problem that is being researched very actively. Rapid development in the sensing technologies has made human activity recognition very important for various fields such as health care, home monitoring, surveillance, etc. In this paper, we describe the use of Classifier Chain method of the Multi-Label Classification approach to tackle the task of multi-resident activity recognition. We evaluate the developed model of Classifier Chain with K-Nearest Neighbor as base classifier on real world ARAS dataset which consists of two smart homes with evaluation metrics such as accuracy, precision and hamming loss. Through results, it can be inferred that Classifier Chain method successfully caters the problem of multi-resident activity recognition taking into consideration underlying label dependencies.

One of the most important applications of the smart home environment is health monitoring and assistance by analysing activities of daily living and here Human Activity Recognition (HAR) plays a major role. The HAR problem, basically a temporal classification problem has been modelled in the past with various methods such as Bayesian Networks, Hidden Markov Model, Conditional Random Field, etc. Here, we propose the Binary Relevance Method of the multi- label classification to tackle the multi-resident activity recognition problem on real world dataset. Through the results obtained by the evaluation metrics namely accuracy, precision and hamming loss, it can be inferred that the model not only computes competitive results to previous works but also signifies the importance of the baseline Binary Relevance method to solve multi-label problems.