Cross-Domain Unseen Activity Recognition Using Transfer Learning
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Author/Creator ORCID
Date
2022-07-01
Type of Work
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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.