Cross-Domain Unseen Activity Recognition Using Transfer Learning

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2208.05006 (1).pdf (1.06 MB)

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https://ieeexplore.ieee.org/document/9842497

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https://doi.org/10.1109/COMPSAC54236.2022.00117
 http://hdl.handle.net/11603/25733

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UMBC Information Systems Department
UMBC Faculty Collection

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Author/Creator ORCID

https://orcid.org/0000-0002-6180-1501

Date

2022-07-01

Type of Work

conference papers and proceedings
postprints
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Citation of Original Publication

M. A. Al Hafiz Khan and N. Roy, "Cross-Domain Unseen Activity Recognition Using Transfer Learning," 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), 2022, pp. 684-693, doi: 10.1109/COMPSAC54236.2022.00117.

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Subjects

Abstract

Activity Recognition (AR) models perform well with a large number of available training instances. However, in the presence of sensor heterogeneity, sensing biasness and variability of human behaviors and activities and unseen activity classes pose key challenges to adopting and scaling these pre-trained activity recognition models in the new environment. These challenging unseen activities recognition problems are addressed by applying transfer learning techniques that leverage a limited number of annotated samples and utilize the inherent structural patterns among activities within and across the source and target domains. This work proposes a novel AR framework that uses the pretrained deep autoencoder model and generates features from source and target activity samples. Furthermore, this AR framework establishes correlations among activities between the source and target domain by exploiting intra- and inter-class knowledge transfer to mitigate the number of labeled samples and recognize unseen activities in the target domain. We validated the efficacy and effectiveness of our AR framework with three real-world data traces (Daily and Sports, Opportunistic, and Wisdm) that contain 41 users and 26 activities in total. Our AR framework achieves performance gains ≈ 5-6% with 111, 18, and 70 activity samples (20% annotated samples) for Das, Opp, and Wisdm datasets. In addition, our proposed AR framework requires 56, 8, and 35 fewer activity samples (10% fewer annotated examples) for Das, Opp, and Wisdm, respectively, compared to the state-of-the-art Untran model.