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.

翻译：高山症是一种可能危及生命的病症，影响众多前往高海拔地区的个体。由于症状可能迅速恶化，及时检测至关重要。早期识别能够实现诸如下降海拔、吸氧或药物治疗等简单干预措施，及时救治可通过显著降低严重并发症风险而挽救生命。尽管传统机器学习技术已应用于利用心率、血氧饱和度、呼吸频率、血压和体温等生理信号识别高山症，但这些方法往往难以在预测性能与低硬件需求之间取得平衡。相比之下，超维计算在此任务中（尤其在生物医学特征有限的情况下）尚未得到充分探索，它可能为现有分类模型提供一种极具吸引力的替代方案。其向量符号框架天然适合硬件高效设计，使其成为可穿戴设备等低功耗系统的理想候选方案。借助轻量级计算和高效流线型内存使用，HDC能够从可穿戴设备采集的生理参数中实现高山症的实时检测，其准确度可与传统机器学习模型相媲美。我们提出AMS-HD，一种集成定制化特征提取与哈达玛HV编码的新型系统，以提升基于HDC的检测精度与效率。该框架非常适合部署于可穿戴健康监测平台，实现对急性高山症的持续、移动式追踪。