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Using a Real-Time Locating System to Measure Walking Activity Associated with Wandering Behaviors Among Institutionalized Older Adults
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A Real-Time Portable IoT System for Telework Tracking.

Yongxin Zhang1, Zheng Chen1, Haoyu Tian1

  • 1Computational Systems Biology, Division of Information Science, Nara Institute of Science and Technology, Nara, Japan.

Frontiers in Digital Health
|October 29, 2021
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Summary

This study developed an Internet of Things (IoT) system using a smartwatch and smartphone to monitor telework patterns. The system accurately recognizes working status, promoting healthier work-from-home behaviors and worker well-being.

Keywords:
convolutional neural networknudgerealtime trackingteleworkwearable

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Area of Science:

  • * Human-Computer Interaction
  • * Wearable Technology
  • * Digital Health

Background:

  • * The rise of telework due to COVID-19 has exacerbated issues like sedentary behavior and reduced physical activity.
  • * Maintaining worker well-being in remote settings requires practical self-management strategies.
  • * Real-time monitoring and behavioral nudging are crucial for regulating work patterns.

Purpose of the Study:

  • * To validate an Internet of Things (IoT) system for real-time telework status monitoring.
  • * To develop a Convolutional Neural Network (CNN) model for accurate action recognition during remote work.
  • * To explore the potential of a user-friendly online system for guiding working patterns and enhancing worker wellness.

Main Methods:

  • * Utilized a smartwatch with accelerometer and gyroscope sensors to collect nine-channel data streams.
  • * Transmitted sensor data to a paired smartphone for real-time preprocessing and action recognition.
  • * Developed and evaluated a shallow Convolutional Neural Network (CNN) model for recognizing common working routines.

Main Results:

  • * The CNN model achieved high accuracy in recognizing working status (e.g., 0.97 recall, 0.98 precision with 5-fold cross-validation).
  • * Performance metrics indicate the feasibility of the fully online monitoring and recognition method.
  • * Compared favorably against Support Vector Machine (SVM) and Random Forest models in preliminary tests.

Conclusions:

  • * The proof-of-concept study demonstrates the potential of an IoT system for tracking telework patterns.
  • * The developed method offers a user-friendly approach to monitor and guide working behaviors.
  • * This system is expected to contribute to worker well-being in both pandemic and post-pandemic remote work environments.