Ergo-PAS: Validation of a smartphone-based real-time posture warning system for ergonomic risk intervention in construction work

Background Construction workers are highly exposed to musculoskeletal disorders (MSDs) owing to awkward and repetitive postures. Traditional ergonomic tools lack real-time feedback, and motion-capture systems are impractical for field use. Smartphone-based inertial measurement units (IMUs) offer a cost-effective and portable alternative to traditional methods. Objective This study aimed to develop and validate the Ergo Posture Analysis System (Ergo-PAS), an Android-based smartphone prototype designed to detect and alert users to high-risk postures in real-time. Method The Ergo-PAS employed synchronized smartphones on the trunk, upper arm, and forearm to capture inertial data during trunk flexion, arm elevation, and repetitive forearm movements. Five participants performed controlled postures, which were validated using a Vicon motion capture system. Pearson's correlation, intraclass correlation coefficients (ICC), and linear regression were applied. Results The Ergo-PAS demonstrated moderate-to-excellent validity and generally good-to-excellent reliability compared with the Vicon motion capture system. Pearson’s correlations ranged from 0.13–0.99 for trunk flexion, 0.50–0.95 for upper-arm elevation, and 0.57–0.94 for forearm movements, while ICC values ranged from 0.08–0.99. The performance varied across body segments. Exploratory linear regression calibration showed stable slopes across participants and positions but with joint-specific intercept differences, indicating the need for tailored calibration. Conclusion This preliminary laboratory validation suggests that the Ergo-PAS can provide reasonably accurate and repeatable posture measurements using smartphone-based inertial sensors. The system shows promise as a low-cost platform for real-time ergonomic risk monitoring and providing feedback. Further research is required to evaluate the usability and effectiveness of these systems during dynamic work activities and in real-world construction environments.

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Work
Published
2026-09-16
DOI
https://doi.org/10.1177/10519815261485981
Primary Topic
Musculoskeletal pain and rehabilitation
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article
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article

Ergo-PAS: Validation of a smartphone-based real-time posture warning system for ergonomic risk intervention in construction work

Auditya Purwandini Sutarto, Yassierli Yassierli, Khoirul Muslim, Wyke Kusmasari et al.
Work
Musculoskeletal pain and rehabilitation
article

Ergo-PAS: Validation of a smartphone-based real-time posture warning system for ergonomic risk intervention in construction work

Auditya Purwandini Sutarto, Yassierli Yassierli, Khoirul Muslim, Wyke Kusmasari, Nugrahaning Sani Dewi, Titah Yudhistira
article en

Abstract

Background Construction workers are highly exposed to musculoskeletal disorders (MSDs) owing to awkward and repetitive postures. Traditional ergonomic tools lack real-time feedback, and motion-capture systems are impractical for field use. Smartphone-based inertial measurement units (IMUs) offer a cost-effective and portable alternative to traditional methods. Objective This study aimed to develop and validate the Ergo Posture Analysis System (Ergo-PAS), an Android-based smartphone prototype designed to detect and alert users to high-risk postures in real-time. Method The Ergo-PAS employed synchronized smartphones on the trunk, upper arm, and forearm to capture inertial data during trunk flexion, arm elevation, and repetitive forearm movements. Five participants performed controlled postures, which were validated using a Vicon motion capture system. Pearson's correlation, intraclass correlation coefficients (ICC), and linear regression were applied. Results The Ergo-PAS demonstrated moderate-to-excellent validity and generally good-to-excellent reliability compared with the Vicon motion capture system. Pearson’s correlations ranged from 0.13–0.99 for trunk flexion, 0.50–0.95 for upper-arm elevation, and 0.57–0.94 for forearm movements, while ICC values ranged from 0.08–0.99. The performance varied across body segments. Exploratory linear regression calibration showed stable slopes across participants and positions but with joint-specific intercept differences, indicating the need for tailored calibration. Conclusion This preliminary laboratory validation suggests that the Ergo-PAS can provide reasonably accurate and repeatable posture measurements using smartphone-based inertial sensors. The system shows promise as a low-cost platform for real-time ergonomic risk monitoring and providing feedback. Further research is required to evaluate the usability and effectiveness of these systems during dynamic work activities and in real-world construction environments.

Work
Bandung Institute of Technology (ID), Airlangga University (ID), National Research and Innovation Agency (ID)
Openalex Percentile: Top 12%
Musculoskeletal pain and rehabilitation
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