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<p>This study presents a cloud-based platform for managing personal digital biomarker data collected from wearable and mobile devices. The platform aims to address four essential challenges for personalized healthcare: ensuring data integrity, supporting various biomarker data types, maintaining measurement reliability, and enabling system scalability. To protect data integrity and privacy, the platform uses private blockchain technology. It also supports heterogeneous data formats, such as time-series ECG signals and dietary images, through standardized interfaces. Measurement reliability is ensured by Gaussian mixture model that successfully detect faulty data with 100% recall. In real-world clinical tests with patients, the platform detects all attempts of data tampering and processes diverse biomarker data with an average latency of less than 1.76 s. The platform’s scalability maintains stable performance under high data loads, and practical use is demonstrated through integration with our mobile application and biomarker web server. We validate the platform by measuring actual biomarker data from real-world atrial fibrillation patients and demonstrate its capability to securely and efficiently integrate wearable biomarker data for personalized healthcare.</p>

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Unified digital biomarker platform for wearable devices assuring integrity, heterogeneity, and scalability

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月27日

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  • Yeonho Yoo,
  • Junseok Lee,
  • Seungwoo Jung,
  • Yunkyung Kang,
  • Dosun Lim,
  • Youngpil Kim,
  • Gyeongsik Yang,
  • Chuck Yoo

摘要

This study presents a cloud-based platform for managing personal digital biomarker data collected from wearable and mobile devices. The platform aims to address four essential challenges for personalized healthcare: ensuring data integrity, supporting various biomarker data types, maintaining measurement reliability, and enabling system scalability. To protect data integrity and privacy, the platform uses private blockchain technology. It also supports heterogeneous data formats, such as time-series ECG signals and dietary images, through standardized interfaces. Measurement reliability is ensured by Gaussian mixture model that successfully detect faulty data with 100% recall. In real-world clinical tests with patients, the platform detects all attempts of data tampering and processes diverse biomarker data with an average latency of less than 1.76 s. The platform’s scalability maintains stable performance under high data loads, and practical use is demonstrated through integration with our mobile application and biomarker web server. We validate the platform by measuring actual biomarker data from real-world atrial fibrillation patients and demonstrate its capability to securely and efficiently integrate wearable biomarker data for personalized healthcare.