Vision-Based Ergonomic Risk Estimation: Deep-learning Strategies

MANLIO MASSIRIS FERNANDEZ; JUAN MIGUEL BAJO; STEVEN MARTINEZ VARGAS; JUAN ÁLVARO FERNANDEZ; CLAUDIO DELRIEUX

Soft Computing Applications

Springer Cham

Año: 2023;

Ergonomic risk assessment is traditionally human-assisted and performed filling forms or through on-site inspection by ergonomists. This frequently leads to inaccuracies due to the subjective bias. Also, it is ineficient and costly given the time and technical knowledge required. Computer-based alternatives are slowly emerging, but so far there is few consensus or uniformity between the underlying practices and technolo- gies. A standardization of data collection in video takes through com- puter vision o↵ers the opportunity to obtain considerable replication levels, which would increase the reliability of the results and the quality of the data available to ergonomists. In this work we propose a workflow that employs two open-source neural networks: STAF for workers’ body joints detection and tracking, and VIBE for 3D movement estimation. Finally, the ergonomic risk is calculated based on REBA, which is one of the most widespread standard in industrial settings. As data collec- tion may be a bottleneck (as usual in deep learning) we propose the use of virtual scenarios generated in Unity3D. This allows to evaluate and quantify several problems associated with actual video takes, including self-occlusions, camera positioning, illumination, noisy backgrounds, and many others. The results are positively conclusive about both the use of this workflow for actual risk assessment, and the feasibility of virtual environments for controlled experimentation.