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Pain expression is characterized by high complexity and multidimensionality. It often uses subjective self-reporting as precise pain assessment remains an essential problem. In this research work, a new methodology for recognition of pain is represented based on a hybrid model that combines convolutional neural networks (CNNs) and particle swarm optimization (PSO) techique along with bidirectional long short- term memory (BiLSTM) networks. This study used video and physiological signals as input data. Experimental results significantly improved the binary pain classification accuracy over several existing models. This study is giving results of 86.3% accuracy and proving the hybrid approach to be very effective. The CNN handles extraction of spatial hierarchies from video data, while the PSO-optimized BiLSTM appropriately models the temporal evolution of physiological responses. Through multimodal integration and advanced optimization approaches, a pathway is paved toward a more reliable and efficient pain recognition model which will eventually help in improving patient care.

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Multimodal Pain Recognition: Integrating Facial Expressions and Biomedical Signals with Deep Learning

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Arhina Ghosh,
  • Neha Tyagi,
  • Nitin Rakesh,
  • Balamurugan Balusamy,
  • Akhil Gupta

摘要

Pain expression is characterized by high complexity and multidimensionality. It often uses subjective self-reporting as precise pain assessment remains an essential problem. In this research work, a new methodology for recognition of pain is represented based on a hybrid model that combines convolutional neural networks (CNNs) and particle swarm optimization (PSO) techique along with bidirectional long short- term memory (BiLSTM) networks. This study used video and physiological signals as input data. Experimental results significantly improved the binary pain classification accuracy over several existing models. This study is giving results of 86.3% accuracy and proving the hybrid approach to be very effective. The CNN handles extraction of spatial hierarchies from video data, while the PSO-optimized BiLSTM appropriately models the temporal evolution of physiological responses. Through multimodal integration and advanced optimization approaches, a pathway is paved toward a more reliable and efficient pain recognition model which will eventually help in improving patient care.