Ergonomic Back Pain Monitoring in Older Workers Using Smart Insoles

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Abstract

This paper presents the design and implementation of a smart insole system aimed at monitoring and reducing back pain and injuries among older workers in physically demanding occupations. The system leverages flexible pressure sensors embedded within insoles to monitor plantar pressure
distribution, providing a less intrusive alternative to traditional back monitoring methods. The collected data is wirelessly transmitted and processed in real time, enabling effective classification of lifting techniques and providing feedback to users. User tests demonstrated high levels of satisfaction and ease of use, indicating the system’s potential for seamless integration into daily activities. The lift classification model achieved an accuracy of 98.8%, highlighting its effectiveness in identifying improper lifting techniques. Power consumption analysis confirmed the system’s efficiency, making it viable for long-term use in the workplace. This study underscores the potential of smart insole technology in occupational health, offering a promising solution
to mitigate back pain and injuries among older workers.
Original languageEnglish
Title of host publicationProceedings of 11th International Conference on Internet of Things: Systems, Management and Security
Number of pages8
PublisherIEEE
Article number5905
Publication statusAccepted/In press - 2024
Event11th International Conference on Internet of Things: Systems, Management and Security - Niagara Building, Malmö, Sweden
Duration: 2 Sept 20245 Sept 2024
https://emergingtechnet.org/IOTSMS2024/index.php

Conference

Conference11th International Conference on Internet of Things: Systems, Management and Security
LocationNiagara Building
Country/TerritorySweden
CityMalmö
Period02/09/202405/09/2024
Internet address

Keywords

  • Smart Insoles
  • Back Pain Monitoring
  • Plantar Pressure Distribution
  • Lift Classification
  • Wearable Sensors
  • IoT
  • Real-time Data Processing
  • Bluetooth Low Energy
  • Cellular IoT
  • LTE-M

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