Ergonomic Back Pain Monitoring in Older Workers Using Smart Insoles

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.

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Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/iotsms62296.2024.10710183
Primary Topic
Ergonomics and Musculoskeletal Disorders
Type
article
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article

Ergonomic Back Pain Monitoring in Older Workers Using Smart Insoles

Martin Nordal Petersen, Sarah Ruepp, Stanley C. Nwabuona, Kartikeya Sharma
Ergonomics and Musculoskeletal Disorders
article

Ergonomic Back Pain Monitoring in Older Workers Using Smart Insoles

Martin Nordal Petersen, Sarah Ruepp, Stanley C. Nwabuona, Kartikeya Sharma
article en

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.

Technical University of Denmark (DK)
Openalex Percentile: Top 77%
Ergonomics and Musculoskeletal Disorders
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