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AMS-HD: Hyperdimensional Computing for Real-Time and Energy-Efficient Acute Mountain Sickness Detection.

Abu Masum, Mehran Moghadam, M Hassan Najafi

    IEEE Transactions on Bio-Medical Engineering
    |June 16, 2026
    PubMed
    Summary
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    A new hyperdimensional computing framework, AMS-HD, enables real-time detection of acute mountain sickness (AMS) using wearable sensors. It offers high accuracy with significantly reduced power and memory requirements for continuous health monitoring.

    Area of Science:

    • Biomedical Engineering
    • Computer Science
    • Altitude Medicine

    Background:

    • Acute mountain sickness (AMS) is a common illness at high altitudes, posing risks of severe health complications.
    • Current machine learning methods for AMS detection lack the efficiency needed for continuous monitoring on wearable devices.

    Purpose of the Study:

    • To introduce AMS-HD, the first hyperdimensional computing (HDC) framework for real-time AMS detection.
    • To develop a hardware-efficient solution for continuous AMS monitoring using wearable physiological signals.

    Main Methods:

    • Developed a complete HDC framework integrating feature selection, hypervector encoding, and positional projection.
    • Implemented and validated the framework on ARM, FPGA, and smartwatch-smartphone platforms.

    Related Experiment Videos

  • Utilized wearable SpO2 and heart rate signals for AMS severity classification.

    • Main Results:

    • AMS-HD achieved up to 91% accuracy and 90% F1-score in binary classification, outperforming SVM and MLP baselines.
    • On FPGA, AMS-HD demonstrated significant reductions in LUT (7.3x) and flip-flop (5.8x) usage, with 3.9x lower power consumption compared to MLP.
    • On mobile platforms, AMS-HD required minimal resources: 1% battery per session, 60 Bytes of memory, and 2.50 ms inference time.

    Conclusions:

    • AMS-HD offers a scalable, hardware-aware alternative for real-time AMS monitoring.
    • The framework achieves competitive performance with substantially lower resource consumption than conventional ML methods.
    • This work bridges wearable inference and low-level hardware deployment for resource-constrained health monitoring.