Improving Resource Efficiency of Deep Activity Recognition via Redundancy Reduction

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)
38 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationHotMobile 2020 - Proceedings of the 21st International Workshop on Mobile Computing Systems and Applications
PublisherACM
Pages33-38
Number of pages6
ISBN (Electronic)9781450371162
DOIs
Publication statusPublished - 3 Mar 2020
MoE publication typeA4 Article in a conference publication
EventInternational Workshop on Mobile Computing Systems and Applications - Austin, United States
Duration: 3 Mar 20204 Mar 2020
Conference number: 21
http://www.hotmobile.org/2020/

Workshop

WorkshopInternational Workshop on Mobile Computing Systems and Applications
Abbreviated titleHotMobile
CountryUnited States
CityAustin
Period03/03/202004/03/2020
Internet address

Keywords

  • Human activity recognition
  • resource-constrained device
  • deep learning

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