PE-USGC: Posture Estimation-Based Unsupervised Spatial Gaussian Clustering for Supervised Classification of Near-Duplicate Human Motion

Details

• Personal Author:
  Iyer H ; Jeong H
• Description:
  Near-duplicate human motion classification presents significant challenges due to the subtle differences and high similarity between actions. This paper introduces a posture estimation-based Gaussian Mixture Model (GMM) clustering algorithm as an enhancement to traditional pixel-based Convolutional Neural Networks (CNNs). The CNN architectures evaluated include ResNet-18, SqueezeNet, DenseNet, and MobileNet, which are used to classify images based on pixel data of images extracted from videos of participants performing chopping, sawing, and slicing tasks. While these CNN models perform well in extracting deep hierarchical features from images, differentiating between near-duplicate tasks can be challenging due to a lack of context in cross-frame human motion. In contrast, the posture-based approach focuses on capturing the spatial and temporal patterns of human body landmarks during task execution, using posture landmark points to classify human motion. Notably, posture-based task classification outperformed pixel-based task classification by 7.2%, with a lesser demand for image frame rate to achieve better accuracy. As the frames per second (FPS) increased from 1 to 30, the accuracy of posture-based classification improved from 76.3% at 1 FPS to 96.97% at 30 FPS. Additionally, we evaluated our model using the UCF Sports Action dataset as a benchmark to compare with state-of-the-art human task classification methods. These comparative analyses highlight the strengths and limitations of each approach, demonstrating that integrating posture data can enhance the classification accuracy of near-duplicate human motion. [Description provided by NIOSH]
• Subjects:
  Coal Coal Mining Dust Mining Occupational Health Safety
• Keywords:
  Algorithms Artificial Intelligence Author Keywords: Posture Landmarks Deep Learning Feature Extraction Frame Rate Optimization Hierarchical Features Human Factors Engineering Image Processing Kinematics-aware Motion Analysis Motion Studies Neural Networks Posture Spatial Clustering Task Classification Temporal Patterns

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• ISSN:
  2169-3536
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Arizona ; Chicago Region [OSHA] ; Illinois ; San Francisco Region [OSHA]
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  12
• NIOSHTIC Number:
  nn:20070309
• Citation:
  IEEE Access 2024 Nov; 12:163093-163108
• Contact Point Address:
  Heejin Jeong, The Polytechnic School, Ira A. Fulton Schools of Engineering, Arizona State University, Mesa, AZ 85212, USA
• Email:
  heejin.jeong@asu.edu
• Federal Fiscal Year:
  2025
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IEEE Access
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:32e1cecaac4d4b04cab4a1e50f1e07d33ea2b277b5135f4f78532826f016b0bd0a9e51158d290a54d1517ad94696845947f2fc0ff6e630c2f36a07c934e98622
• Download URL:
  https://stacks.cdc.gov/view/cdc/208547/cdc_208547_DS1.pdf
• File Type:
  [PDF - 6.48 MB ]