DeActive: Scaling Activity Recognition with Active Deep Learning

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https://dl.acm.org/citation.cfm?id=3214269

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https://doi.org/10.1145/3214269
 http://hdl.handle.net/11603/11042

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UMBC Information Systems Department
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Date

2018

Type of Work

conference papers and proceedings preprints
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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.

Subjects

machine learning
active learning
deep learning architecture
UMBC Mobile Pervasive & Sensor Computing Lab

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.