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.

翻译：目的：急性高原病(AMS)是高原地区最常见的高原病，影响未经习服的登山者（海拔>2500米），并可能发展为危及生命的脑水肿或肺水肿。现有基于可穿戴生理信号的AMS检测方法多采用传统机器学习(ML)，然而这些方法通常难以满足实时连续监测所需的硬件效率。方法：本文提出AMS-HD——首个基于超维计算(HDC)的实时AMS检测框架，涵盖面向移动平台的高层双极性(±1)计算和面向FPGA/ASIC底层二值化(0/1)计算。该框架通过互信息特征选择、超向量编码和位置投影的协同设计，提升分类效率。基于穿戴式SpO2和心率信号，我们在ARM、FPGA和智能手表-智能手机平台上完成验证。结果：在二分类与多分类任务中，AMS-HD均达到或超过SVM与MLP基线性能，二分类准确率最高达91%、F1-score达90%，在外部AMS相关数据集上准确率达85%。在FPGA平台上，AMS-HD的LUT和触发器资源占用较MLP分别降低7.3倍和5.8倍，功耗降低3.9倍。移动平台测试显示，AMS-HD单次推理仅消耗1%电池电量、60字节内存及2.50毫秒——能耗较SVM和MLP分别降低约2倍和3倍以上。结论：AMS-HD为实时AMS监测提供了可扩展的硬件感知型ML替代方案，在大幅降低资源消耗的同时保持竞争性能。意义：本研究首次构建了完整的高原病检测HDC框架，实现了资源受限健康监测场景中可穿戴推理与底层硬件部署的桥接。