Scholarlink

<p>Practicing yoga during pregnancy offers significant physical and mental health benefits for expectant mothers. When incorporated into daily routines, yoga can help mitigate common pregnancy-related discomforts and promote overall well-being. Importantly, trimester-aware yoga recommendations enable women to practice safely according to their stage of pregnancy, preparing both body and mind for a healthier gestational period and childbirth.This study proposes a novel trimester-aware hybrid deep learning framework that integrates multimodal text–video analysis with physiological safety reasoning. The model introduces two key innovations: (i) a Trimester-Weighted Wasserstein Similarity (TW-WD) mechanism for adaptive alignment of user and video embeddings under trimester-specific safety constraints, and (ii) a Safety-Aware Directed Graph Convolutional Relational Neural Network (GCRNN) that propagates health-condition and trimester relationships across the recommendation graph. Experimental results demonstrate an accuracy of 98.3% under fivefold cross-validation and over 97.5% trimester-specific safety compliance, highlighting the framework’s clinical reliability and effectiveness in delivering personalized prenatal yoga recommendations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Trimester-aware yoga video recommendation using hybrid deep learning for pregnant woman

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月26日

查看全文
  • Khushi Bawistale,
  • Surendran Rajendran,
  • Majdi Khalid

摘要

Practicing yoga during pregnancy offers significant physical and mental health benefits for expectant mothers. When incorporated into daily routines, yoga can help mitigate common pregnancy-related discomforts and promote overall well-being. Importantly, trimester-aware yoga recommendations enable women to practice safely according to their stage of pregnancy, preparing both body and mind for a healthier gestational period and childbirth.This study proposes a novel trimester-aware hybrid deep learning framework that integrates multimodal text–video analysis with physiological safety reasoning. The model introduces two key innovations: (i) a Trimester-Weighted Wasserstein Similarity (TW-WD) mechanism for adaptive alignment of user and video embeddings under trimester-specific safety constraints, and (ii) a Safety-Aware Directed Graph Convolutional Relational Neural Network (GCRNN) that propagates health-condition and trimester relationships across the recommendation graph. Experimental results demonstrate an accuracy of 98.3% under fivefold cross-validation and over 97.5% trimester-specific safety compliance, highlighting the framework’s clinical reliability and effectiveness in delivering personalized prenatal yoga recommendations.