Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction

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Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2020-03-03
Major/Subject
Mcode
Degree programme
Language
en
Pages
6
33-38
Series
HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications3 March 2020
Abstract
Compression methods for deep learning have been recently used to port deep neural networks into resource-constrained devices - such as digital gloves and smartwatches - for human activity recognition (HAR). While the results have been in favor of utilizing compressed models, we envision that the current paradigm of long and fixed-size overlapping sliding windows that permeate the literature of HAR contributes negatively toward the goal of more resource-efficient systems, as it induces redundancies in memory and computation. In this work, we provide a different perspective by demonstrating that memory footprint, computational expense, and possibly energy consumption can be dramatically spared by modifying the architecture of the neural networks and their training. It is achieved by enabling non-overlapping short sliding windows and skipping fine-grained features in favor of rough ones on certain occasions, thus reducing the demand for more powerful hardware. Compared with the state-of-the-art, our method is able to achieve comparable performance far more efficiently in terms of resource use.
Description
| openaire: EC/H2020/777222/EU//ATTRACT
Keywords
Human activity recognition, resource-constrained device, deep learning
Other note
Citation
Souza Leite , C & Xiao , Y 2020 , Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction . in HotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications . ACM , pp. 33-38 , International Workshop on Mobile Computing Systems and Applications , Austin , Texas , United States , 03/03/2020 . https://doi.org/10.1145/3376897.3377859