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Work Engagement Recognition in Smart Office.

Congcong Ma1,2, Carman Ka Man Lee1, Juan Du2

  • 1Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.

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|March 14, 2022
PubMed
Summary
This summary is machine-generated.

Smart working increases sedentary behavior, impacting worker health. This study uses infrared sensors to analyze sitting postures, enabling privacy-preserving monitoring and health suggestions for office workers.

Keywords:
Infrared SensorSitting Posture RecognitionSmart OfficeWork Engagement

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Area of Science:

  • Human-Computer Interaction
  • Occupational Health
  • Cyber-Physical Systems

Background:

  • The shift to smart working models due to the COVID-19 pandemic has led to increased sedentary lifestyles among office workers.
  • Physical inactivity and chronic stress associated with remote work threaten workers' physical and psychological well-being, contributing to metabolic disorders, mental illnesses, and musculoskeletal injuries.
  • Cyber-physical systems are integral to modern smart workplaces, enhancing environments and workers through integrated sensing and data processing.

Purpose of the Study:

  • To propose and evaluate a work engagement system for monitoring the psycho-physical comfort of smart working individuals.
  • To recognize worker activities, specifically sitting postures, to assess work engagement levels and provide timely health suggestions.
  • To develop an unobtrusive and privacy-preserving method for sensing worker activities using an infrared sensor array.

Main Methods:

  • Development of a method to recognize sitting postures using an infrared sensor array.
  • Implementation of a system to monitor psycho-physical comfort and assess work engagement based on posture analysis.
  • Experimental evaluation comparing Decision Tree and k-NN classifiers for posture recognition accuracy.
  • Annotation of activities by reviewing video recordings of subjects in a controlled work scenario.

Main Results:

  • The proposed method achieved high recognition rates for eight distinct sitting postures using both Decision Tree and k-NN classifiers.
  • Analysis of sitting postures demonstrated the potential for assessing work engagement and detecting states like boredom or neutral conditions.
  • The system successfully provided suggestions for workers to take breaks when prolonged sedentary postures were detected.

Conclusions:

  • The infrared sensor-based system offers an effective and privacy-preserving solution for monitoring smart workers' psycho-physical comfort and engagement.
  • Posture recognition can serve as a valuable indicator of work engagement and well-being in smart working environments.
  • The system's ability to provide health suggestions based on posture analysis can help mitigate the negative health impacts of sedentary work.