ActiRecognizer: Design and Implementation of a Real-Time Human Activity Recognition System

Liang Cao, Yufeng Wang, Qun Jin, Jianhua Ma

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

2 Citations (Scopus)

Abstract

In our society, inadequate physical activity is one of severe problems issues for human health, which may increase the health risks of many diseases. Nowadays, smartphones are ubiquitous and widely used around the world, in which multi-functional sensors and wireless interfaces are embedded. Therefore, smartphone is viewed as an appropriate platform for real-time activity recognition to address these healthy problems by monitoring and detecting user's everyday activities. In this paper, unlike other wearable devices based applications (e.g., watches, bands, or clip-on devices), ActiRecognizer, a smartphone-based prototype of a real-time human activity recognition (HAR) is designed and implemented, in which a detailed activity report of individuals (i.e. a pie chart containing the proportion and duration of each activity) can be correspondingly generated based on the detected real-time activities. Specifically, ActiRecognizer adopts client/server (C/S) architecture. At client side, smartphone associated with each individual periodically uploads the accelerometer and gyroscope sensing data to server for activity recognition and monitoring. At serve side, HAR is composed of offline training phase and online activity recognition phase: In training phase, sensing data are collected to extract the desired features that can appropriately characterize behaviors, classification model is generated utilizing these features, and then the trained classification model is used to classify user activity in real time. Finally, detailed activity reports and statistics are available to the user via a secure web interface.

Original languageEnglish
Title of host publicationProceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages266-271
Number of pages6
ISBN (Electronic)9781538622094
DOIs
Publication statusPublished - 2017 Jul 1
Event9th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017 - Nanjing, China
Duration: 2017 Oct 122017 Oct 14

Publication series

NameProceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
Volume2018-January

Other

Other9th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017
CountryChina
CityNanjing
Period17/10/1217/10/14

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Keywords

  • Activity recognition
  • data mining
  • machine learning
  • smartphone

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

Cite this

Cao, L., Wang, Y., Jin, Q., & Ma, J. (2017). ActiRecognizer: Design and Implementation of a Real-Time Human Activity Recognition System. In Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017 (pp. 266-271). (Proceedings - 2017 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, CyberC 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CyberC.2017.71