Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning.
Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning.
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DOI：
10.3390/s21196677
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发表时间：
2021-10-08
期刊：
影响因子：
--
通讯作者：
Sun H
中科院分区：
文献类型：
--
作者：
Hajifar S;Lamooki SR;Cavuoto LA;Megahed FM;Sun H
人类活动识别已被广泛用于职业任务的分类。现有的活动识别方法在训练和测试数据遵循相同分布时表现良好。然而，在真实的世界中，由于训练和测试数据之间存在的异质性，这可能会违反该条件，从而导致分类性能的下降。本研究旨在探讨四个异质性来源，跨传感器，跨学科，联合跨传感器和跨学科，跨场景的异质性，对分类性能的影响。为此，我们设计了两个实验，分别称为分离任务情境和混合任务情境，来模拟电力线路工人在不同异质性来源下的任务。此外，一个支持向量机分类器配备了域自适应分类任务和基准对标准的支持向量机基线。我们的研究结果表明，支持向量机配备域自适应优于基线跨传感器，联合跨学科和跨传感器，跨学科的情况下，而支持向量机的性能配备域自适应并没有优于基线跨场景的情况下。因此，研究异构源对分类性能的影响，并在必要时利用领域自适应方法来提高分类性能是非常重要的。
Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.
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影响因子：
3.9
作者：
Banos O;Villalonga C;Garcia R;Saez A;Damas M;Holgado-Terriza JA;Lee S;Pomares H;Rojas I
通讯作者：
Rojas I
影响因子：
10.6
作者：
Abdel-Basset, Mohamed;Hawash, Hossam;Ryan, Michael
通讯作者：
Ryan, Michael
影响因子：
5.1
作者：
Chavarriaga, Ricardo;Sagha, Hesam;Roggen, Daniel
通讯作者：
Roggen, Daniel
影响因子：
3.2
作者：
Hajifar, Sahand;Sun, Hongyue;Cavuoto, Lora A.
通讯作者：
Cavuoto, Lora A.
DOI：
10.1145/3380985
发表时间：
2020-03-01
期刊：
PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT
影响因子：
--
作者：
Chang, Youngjae;Mathur, Akhil;Kawsar, Fahim
通讯作者：
Kawsar, Fahim