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....

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 22; H. 6; S. 2353
Hauptverfasser: Lentzas, Athanasios, Dalagdi, Eleana, Vrakas, Dimitris
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 18.03.2022
MDPI
Schlagworte:
activity recognition
Aged
Aging
Algorithms
ambient sensors
Classification
Decision trees
Deep learning
ensemble learning
Human Activities
Humans
Machine learning
multilabel classification
Neural networks
Sensors
smart home
ensemble learning
smart home
ambient sensors
multilabel classification
activity recognition
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s22062353