A deep learning model for ergonomics risk assessment and sports and health monitoring in self-occluded images

Ergonomic assessments and sports and health monitoring play a crucial role and have contributed to sustainable development in many areas such as product architecture, design, health, and safety as well as workplace design. Recently, visual ergonomic assessments have been broadly employed for skeleto...

Full description

Saved in:
Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2024-03, Vol.18 (2), p.1161-1173
Main Authors: Aghamohammadi, Amirhossein, Beheshti Shirazi, Seyed Aliasghar, Banihashem, Seyed Yashar, Shishechi, Saman, Ranjbarzadeh, Ramin, Jafarzadeh Ghoushchi, Saeid, Bendechache, Malika
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer Imaging
Computer Science
Ergonomics
Frames (data processing)
Image Processing and Computer Vision
Joints (anatomy)
Machine learning
Multimedia Information Systems
Occlusion
Original Paper
Pattern Recognition and Graphics
Risk assessment
Signal,Image and Speech Processing
Sports
Sustainable development
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Ergonomic assessments and sports and health monitoring play a crucial role and have contributed to sustainable development in many areas such as product architecture, design, health, and safety as well as workplace design. Recently, visual ergonomic assessments have been broadly employed for skeleton analysis of human joints for body postures localization and classification to deal with musculoskeletal disorders risks. Moreover, monitoring players in a sports activity helps to analyze their actions to help maximize body performance. However, body postures identification has some limitations in self-occlusion joint postures. In this study, a visual ergonomic assessment technique employing a multi-frame and multi-path convolutional neural network (CNN) is presented to assess ergonomic risks in the presence of free-occlusion and self-occlusion conditions. Our model has four inputs that accept four sequential frames to overcome the problems of the missing joints and classify the input into one of four risk categories. Our pipeline was evaluated on a video with 5 min ~ 300 s (that could be 9000 frames) duration time and showed that our architecture has competitive results (recall = 0.8925, precision = 0.8743, F-score = 0.8837).
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-023-02830-6