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This study introduces an automated method for ergonomic assessment of industrial tasks using a Hidden Markov Model (HMM) for posture classification, applying feature selection techniques to optimize classifier performance. Utilizing the hmmlearn library, the method involves training on labelled data from tasks recorded in a laboratory setting with 10 participants. The model effectively classifies task-related postures, demonstrating its potential for real-time ergonomic risk assessment. Key contributions include the integration of machine learning with ergonomic assessment, highlighting the efficiency of feature selection in improving recognition accuracy.

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Automatic Posture Recognition Method for Industrial Human Ergonomics Assessment Using Visual Sensors Based on Hidden Markov Models

  • 会议论文

  • 发表日期：

    2025年

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  • Yongwei Zhang,
  • Jeff Zhou

摘要

This study introduces an automated method for ergonomic assessment of industrial tasks using a Hidden Markov Model (HMM) for posture classification, applying feature selection techniques to optimize classifier performance. Utilizing the hmmlearn library, the method involves training on labelled data from tasks recorded in a laboratory setting with 10 participants. The model effectively classifies task-related postures, demonstrating its potential for real-time ergonomic risk assessment. Key contributions include the integration of machine learning with ergonomic assessment, highlighting the efficiency of feature selection in improving recognition accuracy.