Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data
Pages 109 - 111
Abstract
In this paper, we propose a method for human activity recognition that can transfer information from multiple sensors to a single sensor with improving noise robustness by using a topological feature representation created by UMAP. With clean data, UMAP improves as much as 12% in macro F1-score compared to the Feature Agglomeration approach. With noisy data, UMAP is more stable, with only a 0.25% decrease in average macro F1-Score. By contrast, with Feature Agglomeration and Traditional approaches, the average macro F1-scores decline by 2.75% and 5.75%, respectively.
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Index Terms
- Improving Noise Robustness of Single Sensor Data in Human Activity Recognition With UMAP and Additional Data
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Published In
September 2022
141 pages
ISBN:9781450394246
DOI:10.1145/3544794
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Published: 27 December 2022
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UbiComp/ISWC '22: The 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing
September 11 - 15, 2022
Cambridge, United Kingdom
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