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

2026-02-09

AI Summary

This work addresses the urgent need for real-time, low-power detection of acute mountain sickness (AMS) in high-altitude environments, where rapid progression demands immediate intervention. Existing machine learning approaches struggle to balance diagnostic accuracy with the stringent computational and memory constraints of wearable devices. To bridge this gap, the study introduces hyperdimensional computing (HDC) to AMS detection for the first time, proposing a lightweight HDC architecture tailored to limited physiological signals—such as heart rate, blood oxygen saturation, and respiratory rate. By integrating customized feature extraction with Hadamard-based hypervector encoding, the method achieves accuracy comparable to conventional models while substantially reducing computational and memory overhead, thereby enabling efficient, real-time AMS monitoring on resource-constrained wearables.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Affective Computing
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Machine learning and data science for the Web

Abstract

Altitude sickness is a potentially life-threatening condition that impacts many individuals traveling to elevated altitudes. Timely detection is critical as symptoms can escalate rapidly. Early recognition enables simple interventions such as descent, oxygen, or medication, and prompt treatment can save lives by significantly lowering the risk of severe complications. Although conventional machine learning (ML) techniques have been applied to identify altitude sickness using physiological signals, such as heart rate, oxygen saturation, respiration rate, blood pressure, and body temperature, they often struggle to balance predictive performance with low hardware demands. In contrast, hyperdimensional computing (HDC) remains under-explored for this task with limited biomedical features, where it may offer a compelling alternative to existing classification models. Its vector symbolic framework is inherently suited to hardware-efficient design, making it a strong candidate for low-power systems like wearables. Leveraging lightweight computation and efficient streamlined memory usage, HDC enables real-time detection of altitude sickness from physiological parameters collected by wearable devices, achieving accuracy comparable to that of traditional ML models. We present AMS-HD, a novel system that integrates tailored feature extraction and Hadamard HV encoding to enhance both the precision and efficiency of HDC-based detection. This framework is well-positioned for deployment in wearable health monitoring platforms, enabling continuous, on-the-go tracking of acute altitude sickness.

Problem

• Acute Mountain Sickness
• Real-Time Detection
• Energy-Efficient
• Wearable Devices
• Hyperdimensional Computing

Innovation

• Hyperdimensional Computing
• Acute Mountain Sickness Detection
• Wearable Health Monitoring
• Hadamard Encoding
• Energy-Efficient ML