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：摘要：目的：急性高山病（AMS）是高海拔地区最常见的疾病，影响未适应环境、攀登至2500米以上的人群，并可能恶化为危及生命的脑水肿或肺水肿。传统基于穿戴式生理信号的AMS检测机器学习方法往往无法满足连续监测所需的实时硬件效率需求。方法：我们提出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/+1）计算以及面向FPGA和ASIC底层的低层二进制（0/1）计算。该框架整合了互信息特征选择、超向量编码与位置投影技术以提升分类效率。验证采用ARM、FPGA及智能手表-智能手机平台，基于可穿戴设备获取的SpO2和心率信号。结果：在二分类与多分类任务中，AMS-HD均达到或超越SVM与MLP基线模型性能，二分类准确率最高达91%、F1分数最高达90%，在外部AMS相关数据集上准确率最高达85%。在FPGA上，AMS-HD的LUT与触发器使用量分别减少7.3倍与5.8倍，功耗较MLP降低3.9倍。在移动平台上，AMS-HD每次会话仅消耗1%电量、60字节内存与2.50毫秒推理时间——能耗较SVM与MLP分别降低约2倍与3倍以上。结论：AMS-HD为实时AMS监测提供了可扩展、硬件感知的替代方案，在显著降低资源消耗的同时保持竞争性性能。意义：本研究首次提出完整的海拔病检测HDC框架，弥合了穿戴式推理与资源受限健康监测中底层硬件部署之间的鸿沟。