How Accurately Can Wearable Sensors Assess Low Back Disorder Risks during Material Handling? Exploring the Fundamental Capabilities and Limitations of Different Sensor Signals

Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2023-02, Vol.23 (4), p.2064
Main Authors: Nurse, Cameron A, Elstub, Laura Jade, Volgyesi, Peter, Zelik, Karl E
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Automation
Biomechanics
Computer Simulation
Data mining
Ergonomics
ergonomics overexertion injuries
Estimates
Foot
Health aspects
Inertial platforms
Insoles
lifting biomechanics
Low back disorders
Lower Extremity
Machine learning
Materials handling
Measuring instruments
Medical research
Medicine, Experimental
Occupational health
Professionals
Risk assessment
Sensors
Simulation
Wearable Electronic Devices
Wearable technology
work-related musculoskeletal disorders
Workers
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Summary:Low back disorders (LBDs) are a leading occupational health issue. Wearable sensors, such as inertial measurement units (IMUs) and/or pressure insoles, could automate and enhance the ergonomic assessment of LBD risks during material handling. However, much remains unknown about which sensor signals to use and how accurately sensors can estimate injury risk. The objective of this study was to address two open questions: (1) How accurately can we estimate LBD risk when combining trunk motion and under-the-foot force data (simulating a trunk IMU and pressure insoles used together)? (2) How much greater is this risk assessment accuracy than using only trunk motion (simulating a trunk IMU alone)? We developed a data-driven simulation using randomized lifting tasks, machine learning algorithms, and a validated ergonomic assessment tool. We found that trunk motion-based estimates of LBD risk were not strongly correlated (r range: 0.20-0.56) with ground truth LBD risk, but adding under-the-foot force data yielded strongly correlated LBD risk estimates (r range: 0.93-0.98). These results raise questions about the adequacy of a single IMU for LBD risk assessment during material handling but suggest that combining an IMU on the trunk and pressure insoles with trained algorithms may be able to accurately assess risks.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23042064