Comparing Optical and Custom IoT Inertial Motion Capture Systems for Manual Material Handling Risk Assessment Using the NIOSH Lifting Index DOI Creative Commons
Manuel Gutiérrez, Britam Gómez,

Gustavo Retamal

et al.

Technologies, Journal Year: 2024, Volume and Issue: 12(10), P. 180 - 180

Published: Sept. 30, 2024

Assessing musculoskeletal disorders (MSDs) in the workplace is vital for improving worker health and safety, reducing costs, increasing productivity. Traditional hazard identification methods are often inefficient, particularly detecting complex risks, which may compromise risk management. This study introduces a semi-automatic platform using two motion capture systems—an optical system (OptiTrack®) Bluetooth Low Energy (BLE)-based with inertial measurement units (IMUs), developed at Biomedical Engineering Laboratory, Universidad de Concepción, Chile. These systems, tested on 20 participants (10 women 10 men, aged 30 ± 9 years without MSDs), facilitate assessments via digitized NIOSH Index method. Analysis of ergonomically significant variables (H, V, A, D) calculation RWL LI showed both systems aligned expected ergonomic standards, although differences were observed vertical displacement (V), horizontal (H), trunk rotation (A), indicating areas improvement, especially BLE system. The Inertial MoCap recorded mean heights 33.87 cm (SD = 4.46) displacements 13.17 4.75), while OptiTrack® 30.12 2.91) 15.67 2.63). Despite greater variability measurements, accurately captured absolute (D), means 32.05 7.36) 31.80 3.25) OptiTrack®. Performance analysis high precision achieving rates 98.5%. Sensitivity, however, was lower (97.5%) compared to (98.7%). system’s F1 score 97.9%, scored 98.6%, can reliably assess risk. findings demonstrate potential BLE-based IMUs ergonomics, though further improvements accuracy needed. user-friendly significantly enhance assessment efficiency across various environments.

Language: Английский

Combining Postural Sway Parameters and Machine Learning to Assess Biomechanical Risk Associated with Load-Lifting Activities DOI Creative Commons
G. Prisco, Maria Agnese Pirozzi,

Antonella Santone

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(1), P. 105 - 105

Published: Jan. 4, 2025

Background/Objectives: Long-term work-related musculoskeletal disorders are predominantly influenced by factors such as the duration, intensity, and repetitive nature of load lifting. Although traditional ergonomic assessment tools can be effective, they often challenging complex to apply due absence a streamlined, standardized framework. Recently, integrating wearable sensors with artificial intelligence has emerged promising approach effectively monitor mitigate biomechanical risks. This study aimed evaluate potential machine learning models, trained on postural sway metrics derived from an inertial measurement unit (IMU) placed at lumbar region, classify risk levels associated lifting based Revised NIOSH Lifting Equation. Methods: To compute parameters, IMU captured acceleration data in both anteroposterior mediolateral directions, aligning closely body’s center mass. Eight participants undertook two scenarios, each involving twenty consecutive tasks. classifiers were tested utilizing validation strategies, Gradient Boost Tree algorithm achieving highest accuracy Area under ROC Curve 91.2% 94.5%, respectively. Additionally, feature importance analysis was conducted identify most influential parameters directions. Results: The results indicate that combination model offers feasible for predicting risks Conclusions: Further studies broader participant pool varied conditions could enhance applicability this method occupational ergonomics.

Language: Английский

Citations

0
The Role of Deep Learning and Gait Analysis in Parkinson’s Disease: A Systematic Review DOI Creative Commons
Alessandra Franco, Michela Russo, Marianna Amboni

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(18), P. 5957 - 5957

Published: Sept. 13, 2024

Parkinson's disease (PD) is the second most common movement disorder in world. It characterized by motor and non-motor symptoms that have a profound impact on independence quality of life people affected disease, which increases caregivers' burdens. The use quantitative gait data with PD deep learning (DL) approaches based are emerging as increasingly promising methods to support aid clinical decision making, aim providing objective diagnosis, well an additional tool for monitoring. This will allow early detection assessment progression, implementation therapeutic interventions. In this paper, authors provide systematic review DL techniques recently proposed analysis using Preferred Reporting Items Systematic Reviews Meta-Analyses (PRISMA) guidelines. Scopus, PubMed, Web Science databases were searched across interval six years (between 2018, when first article was published, 2023). A total 25 articles included review, reports studies patients both wearable non-wearable sensors. Additionally, these employed networks classification, monitoring purposes. demonstrate there wide employment field convolutional neural analyzing signals from sensors pose estimation motion videos. addition, discuss current difficulties highlight future solutions progression.

Language: Английский

Citations

3
Validity of Wearable Inertial Sensors for Gait Analysis: A Systematic Review DOI Creative Commons
G. Prisco, Maria Agnese Pirozzi, Antonella Santone

et al.

Diagnostics, Journal Year: 2024, Volume and Issue: 15(1), P. 36 - 36

Published: Dec. 27, 2024

Background/Objectives: Gait analysis, traditionally performed with lab-based optical motion capture systems, offers high accuracy but is costly and impractical for real-world use. Wearable technologies, especially inertial measurement units (IMUs), enable portable accessible assessments outside the lab, though challenges sensor placement, signal selection, algorithm design can affect accuracy. This systematic review aims to bridge benchmarking gap between IMU-based traditional validating use of wearable systems gait analysis. Methods: examined English studies 2012 2023, retrieved from Scopus database, comparing sensors focusing on IMU body parameters, validation metrics. Exclusion criteria search included conference papers, reviews, unavailable without those not involving agreement or systems. Results: From an initial pool 479 articles, 32 were selected full-text screening. Among them, lower resulted in most common site single placement (in 22 studies), while frequently used multi-sensor configuration involved positioning back, shanks, feet, thighs (10 studies). Regarding 11 out focused spatial-temporal 12 joint kinematics, 2 events, remainder a combination parameters. In terms metrics, 24 employed correlation coefficients as primary measure, 7 error coefficients, Bland–Altman Validation metrics revealed that IMUs exhibited good moderate kinematic measures. contrast, spatiotemporal parameters demonstrated greater variability, ranging poor. Conclusions: highlighted transformative potential advancing analysis beyond constraints laboratory-based

Language: Английский

Citations

3
Comparing Optical and Custom IoT Inertial Motion Capture Systems for Manual Material Handling Risk Assessment Using the NIOSH Lifting Index DOI Creative Commons
Manuel Gutiérrez, Britam Gómez,

Gustavo Retamal

et al.

Technologies, Journal Year: 2024, Volume and Issue: 12(10), P. 180 - 180

Published: Sept. 30, 2024

Assessing musculoskeletal disorders (MSDs) in the workplace is vital for improving worker health and safety, reducing costs, increasing productivity. Traditional hazard identification methods are often inefficient, particularly detecting complex risks, which may compromise risk management. This study introduces a semi-automatic platform using two motion capture systems—an optical system (OptiTrack®) Bluetooth Low Energy (BLE)-based with inertial measurement units (IMUs), developed at Biomedical Engineering Laboratory, Universidad de Concepción, Chile. These systems, tested on 20 participants (10 women 10 men, aged 30 ± 9 years without MSDs), facilitate assessments via digitized NIOSH Index method. Analysis of ergonomically significant variables (H, V, A, D) calculation RWL LI showed both systems aligned expected ergonomic standards, although differences were observed vertical displacement (V), horizontal (H), trunk rotation (A), indicating areas improvement, especially BLE system. The Inertial MoCap recorded mean heights 33.87 cm (SD = 4.46) displacements 13.17 4.75), while OptiTrack® 30.12 2.91) 15.67 2.63). Despite greater variability measurements, accurately captured absolute (D), means 32.05 7.36) 31.80 3.25) OptiTrack®. Performance analysis high precision achieving rates 98.5%. Sensitivity, however, was lower (97.5%) compared to (98.7%). system’s F1 score 97.9%, scored 98.6%, can reliably assess risk. findings demonstrate potential BLE-based IMUs ergonomics, though further improvements accuracy needed. user-friendly significantly enhance assessment efficiency across various environments.

Language: Английский

Citations

1