Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

As the world's population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the literature focus on single-resident activity recognition. While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). In this study, an experimental comparison between different MLC algorithms is attempted. Three different techniques were implemented: RAkEL d , classifier chains, and binary relevance. These methods are evaluated using the ARAS and CASAS public datasets. Results obtained from experiments have shown that using MLC can recognize activities performed by multiple people with high accuracy. While RAkEL(d )had the best performance, the rest of the methods had on-par results.

Automated summary

As the population ages and access to ambient sensors becomes easier, activity recognition in smart home installations has gained increased scientific interest. While most previous studies focused on recognizing activities of single residents, this study investigates activity recognition for multiple residents concurrently, treating it as a multilabel classification problem. Experimental comparison of different algorithms showed that using multilabel classification can accurately recognize activities performed by multiple people.