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

Abstract

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: RAkELd, 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 RAkELd had the best performance, the rest of the methods had on-par results.

Multi-Sensors for Human Activity Recognition

“…This review [ 3 ] presents the challenges of the applications of HAR in smart homes, the algorithms and works in this field and any identified gaps. The authors divide the HAR systems into two categories: video-based and sensor-based systems.…”

Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context

“…The recognition of human activities is fundamental to many services [ 8 , 9 ], but providing sufficiently robust HAR systems that could be deployed in an ordinary real environment remains a major challenge. The existing works in the literature have focused mainly on recognition through pre-segmented sensor data (i.e., dividing the data stream into segments, each defined by its beginning and its end) [ 10 ].…”