Exposures to select risk factors can be estimated from a continuous stream of inertial sensor measurements during a variety of lifting-lowering tasks

Wearable inertial measurement units (IMUs) are used increasingly to estimate biomechanical exposures in lifting-lowering tasks. The objective of the study was to develop and evaluate predictive models for estimating relative hand loads and two other critical biomechanical exposures to gain a compreh...

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Bibliographic Details
Published in:Ergonomics Vol. 67; no. 11; pp. 1596 - 1611
Main Author: Lim, Sol
Format: Journal Article
Language:English
Published: England Taylor & Francis 01.11.2024
Taylor & Francis LLC
Subjects:
Accelerometry - instrumentation
Adult
Algorithms
biomechanical exposures
Biomechanical Phenomena
Biomechanics
data-driven algorithms
Errors
Exposure
Female
Hand - physiology
Handling
Hoisting
Humans
Inertial platforms
Inertial sensing devices
Inertial sensor measurements
Kinematics
Lifting
lifting-lowering
Male
Manual lifting
Musculoskeletal diseases
Musculoskeletal Diseases - etiology
Musculoskeletal Diseases - prevention & control
Occupational Diseases - etiology
Occupational Diseases - prevention & control
Phases
Prediction models
Risk Factors
risk factors estimation
Task Performance and Analysis
Wearable Electronic Devices
Weight-Bearing - physiology
Work stations
Workstations
Young Adult
Inertial sensor measurements
biomechanical exposures
risk factors estimation
data-driven algorithms
lifting-lowering
ISSN:0014-0139, 1366-5847, 1366-5847
Online Access:Get full text
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Description
Summary:Wearable inertial measurement units (IMUs) are used increasingly to estimate biomechanical exposures in lifting-lowering tasks. The objective of the study was to develop and evaluate predictive models for estimating relative hand loads and two other critical biomechanical exposures to gain a comprehensive understanding of work-related musculoskeletal disorders in lifting. We collected 12,480 lifting-lowering phases from 26 subjects (15 men and 11 women) performing manual lifting-lowering tasks with hand loads (0-22.7 kg) at varied workstation heights and handling modes. We implemented a Hierarchical model, that sequentially classified risk factors, including workstation height, handling mode, and relative hand load. Our algorithm detected lifting-lowering phases (>97.8%) with mean onset errors of 0.12 and 0.2 seconds for lifting and lowering phases. It estimated workstation height (>98.5%), handling mode (>87.1%), and relative hand load (mean absolute errors of 5.6-5.8%) across conditions, highlighting the benefits of data-driven models in deriving lifting-lowering occurrences, timing, and critical risk factors from continuous IMU-based kinematics. The study developed and validated algorithms for detecting and predicting exposure to various risk factors during diverse lifting-lowering tasks. These factors encompass the occurrence, timing, workstation height, handling mode, and relative hand position. This approach facilitates the extraction of contextual information related to lifting tasks conducted in real-world settings through a continuous stream of inertial sensor measurements. Consequently, it can enable automated risk assessment for lifting activities in the field.
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ISSN:0014-0139
1366-5847
1366-5847
DOI:10.1080/00140139.2024.2343949