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Advancements in AI-Driven Ergonomics: Enhancing Health and Safety in the Digital Age

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Dr. Gyula Szabó

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Donát Bánki Faculty of Mechanical and Safety Engineering, Óbuda University, 1081 Budapest, Hungary
Interests: workplace design methodology; ergonomics

Special Issue Information

Dear Colleagues,

Significant advances in information technology have occurred since the beginning of the new artificial intelligence (AI) era, affecting many aspects of human existence, including work and daily activities. The extensive use of computer devices and software has resulted in faster access to a wide range of data, improved information processing, and automated production processes. However, greater connection with electronic gadgets may pose health hazards in terms of physiological, psychological, and social factors. This Special Issue intends to address these health issues using methodologies and techniques applying AI and human–computer interaction, ergonomics, and health.

In addition to physical concerns, the area of workplace ergonomics and safety has developed to incorporate cognitive and psychosocial components. Cognitive burden, mental health, and psychosocial problems have all been popular research topics. Given the potential consequences, prevention techniques for both physical and cognitive safety and health issues are critical.

The Special Issue welcomes articles on various research areas, such as risk assessment, workplace safety and health, safety culture, work performance, ageing workforce, organizational safety, human–system interface, work environments, human–computer interaction and ergonomics and health and wearable technology.

Dr. Gyula Szabó
Guest Editor

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Keywords

occupational ergonomics
safety and health
ageing workforce
musculoskeletal disorders (MSDs)
occupational health and safety
waste management
industrial ergonomics
ergonomics for sustainable workplaces
human-centered design
wearable technology
biomechanics
data-driven decision making
task analysis
human performance
human–computer interaction (HCI)
ergonomic design and optimization
artificial intelligence
computer simulations of work and environment

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15 pages, 1171 KiB

Open AccessArticle

Can Machine Learning Enhance Computer Vision-Predicted Wrist Kinematics Determined from a Low-Cost Motion Capture System?

by Joel Carriere, Michele L. Oliver, Andrew Hamilton-Wright, Calvin Young and Karen D. Gordon

Appl. Sci. 2025, 15(7), 3552; https://doi.org/10.3390/app15073552 - 24 Mar 2025

Cited by 1 | Viewed by 437

Abstract

Wrist kinematics can provide insight into the development of repetitive strain injuries, which is important particularly in workplace environments. The emergence of markerless motion capture is beginning to revolutionize kinematic assessment such that it can be conducted outside of the laboratory. The purpose of this work was to apply open-source software (OSS) and machine learning (ML) by using DeepLabCut (OSS) to determine anatomical landmark locations and a variety of regression algorithms and neural networks to predict wrist angles. Sixteen participants completed a series of flexion–extension (FE) and radial–ulnar (RUD) range-of-motion (ROM) trials that were captured using a 13-camera VICON optical motion capture system (i.e., the gold standard), as well as 4 GoPro video cameras. DeepLabCut (version 2.3.3) was used to generate a 2D dataset of anatomical landmark coordinates from video obtained from one obliquely oriented GoPro video camera. Anipose (version 1.0.1) was used to generate a 3D dataset from video obtained from four GoPro cameras. Anipose and various ML algorithms were used to determine RUD and FE wrist angles. The algorithms were trained and tested using a 75%:25% data split with four folds for the 2D and 3D datasets. Of the seven ML techniques applied, deep neural networks resulted in the highest prediction accuracy (5.5) for both the 2D and 3D datasets. This was substantially higher than the wrist angle prediction accuracy provided by Anipose (

F E - 99

;

R U D - 25.2

). We found that, excluding cubic regression, all other studied algorithms exhibited reasonable performance that was similar to that reported by previous authors, showing that it is indeed possible to predict wrist kinematics using a low-cost motion capture system. In agreement with past research, the increased MAE for FE is thought to be due to a larger ROM. Full article

(This article belongs to the Special Issue Advancements in AI-Driven Ergonomics: Enhancing Health and Safety in the Digital Age)

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