Faculty of Sciences, School of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
Sensors (Basel). 2022 Mar 18;22(6):2353. doi: 10.3390/s22062353.
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: , 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 had the best performance, the rest of the methods had on-par results.
随着世界人口老龄化，以及近年来人们更容易获得环境传感器，智能家居环境中的活动识别引起了科学界越来越多的兴趣。文献中的大多数已发表论文都集中在单人活动识别上。虽然这是一个重要的领域，特别是在关注独居的老年人时，但多居民活动识别在智能家居中可能有更多的应用。同时识别多个居民的活动可以被视为多标签分类问题 (MLC)。在这项研究中，尝试对不同的 MLC 算法进行实验比较。实现了三种不同的技术：分类器链、二进制相关性。使用 ARAS 和 CASAS 公共数据集评估这些方法。实验结果表明，使用 MLC 可以非常准确地识别多个人执行的活动。虽然表现最好，但其他方法的结果也相当。
