Compensation Method for Missing and Misidentified Skeletons in Nursing Care Action Assessment by Improving Spatial Temporal Graph Convolutional Networks

With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty whe...

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Bibliographic Details
Published in:Bioengineering (Basel) 2024-01, Vol.11 (2), p.127
Main Authors: Han, Xin, Nishida, Norihiro, Morita, Minoru, Sakai, Takashi, Jiang, Zhongwei
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Cameras
Caregivers
Comparative analysis
Compensation
ergonomic posture risk assessment
Ergonomics
Health aspects
Human resource management
Kinematics
Medical care
Medical personnel
Medical research
Medicine, Experimental
Methods
Musculoskeletal diseases
Nursing
Nursing care
Nursing homes
Occupational health and safety
Pose estimation
Prevention
Quality management
REBA
Risk assessment
Sensors
Skeleton
skeleton compensation
ST-GCN
Statistics
Trends
work-related musculoskeletal disorders
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Summary:With the increasing aging population, nursing care providers have been facing a substantial risk of work-related musculoskeletal disorders (WMSDs). Visual-based pose estimation methods, like OpenPose, are commonly used for ergonomic posture risk assessment. However, these methods face difficulty when identifying overlapping and interactive nursing tasks, resulting in missing and misidentified skeletons. To address this, we propose a skeleton compensation method using improved spatial temporal graph convolutional networks (ST-GCN), which integrates kinematic chain and action features to assess skeleton integrity and compensate for it. The results verified the effectiveness of our approach in optimizing skeletal loss and misidentification in nursing care tasks, leading to improved accuracy in calculating both skeleton joint angles and REBA scores. Moreover, comparative analysis against other skeleton compensation methods demonstrated the superior performance of our approach, achieving an 87.34% REBA accuracy score. Collectively, our method might hold promising potential for optimizing the skeleton loss and misidentification in nursing care tasks.
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering11020127