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<p>High rates of missing data in wearable sensor streams hinder early detection of infectious diseases, especially in low-resource settings with inconsistent device adherence and connectivity. We developed a lightweight generative adversarial network (GAN) framework that imputes missing heart rate data and integrates with a rule-based anomaly detection algorithm to identify early signs of infection. In a cohort from rural Kenya (<i>n</i> = 300, 161 malaria-positives), our system triggered early alerts in 100 cases, including 42 solely with imputation. Alerts preceded symptom onset by 11.9 days, aligning with the 11.7-day parasitemia window from controlled trials. Despite 50% data coverage, alerts occurred on 3.5 consecutive days during the infection window, improving early detection by 35%. The GAN, trained only on external COVID-19 data (<i>n</i> = 3318), generalized to malaria, reducing reconstruction error by 58%. This approach demonstrates scalable, cross-pathogen physiological monitoring and offers a robust tool for disease surveillance in settings challenged by high wearable data loss.</p>

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Overcoming Data Loss in Wearable Disease Detection with GAN-Based Imputation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Jürgen Wallner,
  • Sarah Berbuir,
  • Lukas Birner,
  • Adrian Dendorfer,
  • Bivek Panthi,
  • Beatrix Rahnsch,
  • Julius Muma,
  • Stephen Munga,
  • David Obor,
  • Till Bärnighausen,
  • Sandra Barteit

摘要

High rates of missing data in wearable sensor streams hinder early detection of infectious diseases, especially in low-resource settings with inconsistent device adherence and connectivity. We developed a lightweight generative adversarial network (GAN) framework that imputes missing heart rate data and integrates with a rule-based anomaly detection algorithm to identify early signs of infection. In a cohort from rural Kenya (n = 300, 161 malaria-positives), our system triggered early alerts in 100 cases, including 42 solely with imputation. Alerts preceded symptom onset by 11.9 days, aligning with the 11.7-day parasitemia window from controlled trials. Despite 50% data coverage, alerts occurred on 3.5 consecutive days during the infection window, improving early detection by 35%. The GAN, trained only on external COVID-19 data (n = 3318), generalized to malaria, reducing reconstruction error by 58%. This approach demonstrates scalable, cross-pathogen physiological monitoring and offers a robust tool for disease surveillance in settings challenged by high wearable data loss.