Smartphone-Aided Human Activity Recognition Method using Residual Multi-Layer Perceptron

Shang Shi, Yu Wang, Heng Dong, Guan Gui, Tomoaki Ohtsuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Human activity recognition (HAR) has received intensely attention in many applications, such as healthcare, human-computer interaction, and smart home. Existing HAR methods based on deep learning (DL) have been proposed in the last several years. However, these DL-based HAR methods are hard to balance between performance and cost, which truly limited the applications in practical scenarios. To solve this problem, this paper proposes a smartphone-aided HAR method using the residual multi-layer perceptron (Res-MLP). It composes of two linear layers and Gaussian error linear unit (GELU) activation function, and obtains Res-MLP network through residual. Experimental results show that the proposed HAR method can achieve a high classification accuracy of 96.72% based on the public UCI HAR dataset.

Original languageEnglish
Title of host publicationINFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665409261
DOIs
Publication statusPublished - 2022
Event2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 - Virtual, Online, United States
Duration: 2022 May 22022 May 5

Publication series

NameINFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops

Conference

Conference2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022
Country/TerritoryUnited States
CityVirtual, Online
Period22/5/222/5/5

Keywords

  • deep learning
  • Human activity recognition
  • multi-layer perceptron
  • residual network

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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