DeActive: Scaling Activity Recognition with Active Deep Learning
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2018
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Citation of Original Publication
H. M. Sajjad Hossain, MD Abdullah Al Haiz Khan, and Nirmalya Roy. 2018. DeActive: Scaling Activity Recognition with Active Deep Learning. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 2, Article 66 (July 2018), 23 pages. DOI: https://doi.org/10.1145/3214269
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This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
Abstract
Deep learning architectures have been applied increasingly in multi-modal problems which has empowered a large number
of application domains needing much less human supervision in the process. As unlabeled data are abundant in most of the
application domains, deep architectures are getting increasingly popular to extract meaningful information out of these large
volume of data. One of the major caveat of these architectures is that the training phase demands both computational time
and system resources much higher than shallow learning algorithms and it is posing a diffcult challenge for the researchers to
implement the architectures in low-power resource constrained devices. In this paper, we propose a deep and active learning
enabled activity recognition model, DeActive, which is optimized according to our problem domain and reduce the resource
requirements. We incorporate active learning in the process to minimize the human supervision along with the effort needed
for compiling ground truth. the DeActive model has been validated using real data traces from a retirement community center
(IRB #HP-00064387) and 4 public datasets. Our experimental results show that our model can contribute better accuracy while
ensuring less amount of resource usages in reduced time compared to other traditional deep learning approaches in activity
recognition.