A systematic literature review of computer vision-based biomechanical models for physical workload estimation

D Egeonu, B Jia - Ergonomics, 2024 - Taylor & Francis
Ergonomic risks, driven by strenuous physical demands in complex work settings, are
prevalent across industries. Addressing these challenges through detailed assessment and …

[HTML][HTML] Worker's physical fatigue classification using neural networks

E Escobar-Linero, M Domínguez-Morales… - Expert Systems with …, 2022 - Elsevier
Physical fatigue is not only an indication of the user's physical condition and/or need for
sleep or rest, but can also be a significant symptom of various diseases. This fatigue affects …

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

S Lim - Ergonomics, 2024 - Taylor & Francis
Wearable inertial measurement units (IMUs) are used increasingly to estimate
biomechanical exposures in lifting-lowering tasks. The objective of the study was to develop …

Machine learning for detection and risk assessment of lifting action

B Thomas, ML Lu, R Jha… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Repetitive occupational lifting has been shown to create an increased risk for incidence of
back pain. Ergonomic workstations that promote proper lifting technique can reduce risk, but …

Characterizing human box-lifting behavior using wearable inertial motion sensors

SD Hlucny, D Novak - Sensors, 2020 - mdpi.com
Although several studies have used wearable sensors to analyze human lifting, this has
generally only been done in a limited manner. In this proof-of-concept study, we investigate …

Tactile Gloves Predict Load Weight During Lifting With Deep Neural Networks

G Zhou, ML Lu, D Yu - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Overexertion in lifting tasks is one of the leading causes of occupational injuries. The load
weight is the key information required to evaluate the risk of a lifting task. However, weight …

A computer vision approach for estimating lifting load contributors to injury risk

G Zhou, V Aggarwal, M Yin, D Yu - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Safety practitioners widely use the lifting index (LI) to determine workers' lifting risk but are
hampered by the difficulties of estimating the lifting load without intervention or intrusive …

Estimating trunk angle kinematics during lifting using a computationally efficient computer vision method

RL Greene, ML Lu, MS Barim, X Wang… - Human …, 2022 - journals.sagepub.com
Objective A computer vision method was developed for estimating the trunk flexion angle,
angular speed, and angular acceleration by extracting simple features from the moving …

Use of a wearable electromyography armband to detect lift-lower tasks and classify hand loads

S Taori, S Lim - Applied Ergonomics, 2024 - Elsevier
We used an armband with embedded surface electromyography (sEMG) electrodes,
together with machine-learning (ML) models, to automatically detect lifting-lowering activities …

A deep learning approach for lower back-pain risk prediction during manual lifting

K Snyder, B Thomas, ML Lu, R Jha, MS Barim… - Plos one, 2021 - journals.plos.org
Occupationally-induced back pain is a leading cause of reduced productivity in industry.
Detecting when a worker is lifting incorrectly and at increased risk of back injury presents …