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

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

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルINFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781665409261
DOI
出版ステータスPublished - 2022
イベント2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022 - Virtual, Online, United States
継続期間: 2022 5月 22022 5月 5

出版物シリーズ

名前INFOCOM WKSHPS 2022 - IEEE Conference on Computer Communications Workshops

Conference

Conference2022 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2022
国/地域United States
CityVirtual, Online
Period22/5/222/5/5

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理

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