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

TL;DR

AMS-HD introduces a hyperdimensional computing framework for real-time, energy-efficient detection of acute mountain sickness using wearable sensors, outperforming traditional ML methods in resource-constrained environments.

Contribution

This work is the first to develop a complete HDC-based system for AMS detection, optimized for mobile and low-power hardware platforms.

Findings

Achieves up to 91% accuracy and 90% F1-score in binary classification.

Reduces FPGA resource usage by over 7x and power consumption by 3.9x compared to MLP.

Requires only 1% battery per session and 2.50 ms inference time on mobile devices.

Abstract

Objective: Acute mountain sickness (AMS) is the most prevalent altitude illness, affecting unacclimatized individuals ascending above 2,500 m and potentially escalating to life threatening cerebral or pulmonary edema. Conventional machine learning (ML) methods for AMS detection from wearable physiological signals often fail to meet real-time hardware efficiency requirements of continuous monitoring. Methods: We present AMS-HD, the first hyperdimensional computing (HDC)-based framework for real-time AMS detection, spanning high- level bipolar (-1/+1) computing for mobile platforms and low-level binary (0/1) computing for FPGA and ASIC targets. The framework integrates mutual information feature selection, hypervector encoding, and positional projection to enhance classification efficiency. Validation spans ARM, FPGA, and smartwatch-smartphone platforms using wearable-accessible SpO2 and heart rate signals. Results: AMS-HD matches or outperforms SVM and MLP baselines in both binary and multiclass classification, achieving up to 91% accuracy and 90% F1-score in binary classification, and up to 85% accuracy on external AMS-related datasets. On FPGA, AMS-HD reduces LUT and flip-flop usage by 7.3x and 5.8x, while consuming 3.9x less power than MLP. On mobile platforms, AMS-HD requires only 1% battery per session, 60 Bytes of memory, and 2.50 ms inference time--approximately 2x and more than 3x lower energy consumption than SVM and MLP. Conclusion: AMS-HD provides a scalable, hardware-aware alternative to conventional ML for real-time AMS monitoring, achieving competitive performance with substantially lower resource consumption. Significance: This work presents the first complete HDC framework for altitude sickness detection, bridging wearable inference and low-level hardware deployment for resource-constrained health monitoring.