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This work introduces a deep learning-based framework for 3D pressure mapping to assess sleep quality and body strain. 2D pressure maps suffer from loss of depth information, poor spatial context, posture misclassification errors, and limited accuracy in capturing regional pressure variations. To overcome these limitations, the framework constructs 3D pressure maps that enable precise region-wise pressure estimation with anatomical landmarks to analyze body strain. Sleep quality is monitored by tracking frequent posture changes with converting pressure maps to point clouds achieved 99.26% accuracy with PointNet and 99.49% with PointCNN.

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Sleep Quality and Body Strain Assessment through 3D Pressure Mapping Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Deepesh Sudhan Arunachalam,
  • Dennis Andrew,
  • K. S. Gayathri,
  • A. Shahina,
  • V. Durgadevi,
  • A. Saravanan

摘要

This work introduces a deep learning-based framework for 3D pressure mapping to assess sleep quality and body strain. 2D pressure maps suffer from loss of depth information, poor spatial context, posture misclassification errors, and limited accuracy in capturing regional pressure variations. To overcome these limitations, the framework constructs 3D pressure maps that enable precise region-wise pressure estimation with anatomical landmarks to analyze body strain. Sleep quality is monitored by tracking frequent posture changes with converting pressure maps to point clouds achieved 99.26% accuracy with PointNet and 99.49% with PointCNN.