Document Type
Conference Proceeding
Publication Date
5-25-2025
Manuscript Version
am
Abstract
Acute mountain sickness (AMS) is a potentially life-threatening condition that affects many individuals traveling to high altitudes. Early diagnosis is crucial, especially for travelers who may not have immediate access to medical resources. While traditional machine learning (ML) methods have been used to detect AMS using biomedical data (e.g., heart rate, blood oxygen saturation, respiration rate, blood pressure, and body temperature), hyperdimensional computing (HDC) has yet to be explored for this purpose using the few of biomedical data. Previous classification methods fall short of balancing accuracy with low hardware complexity, but HDC offers a promising solution. HDC provides a hardware-efficient alternative solution, making it well-suited for resource-constrained environments, such as wearable devices. Its lightweight architecture and efficient memory management make it ideal for embedded systems, enabling real-time AMS detection with accuracy comparable to traditional ML models. We introduce AMS-HD, a novel framework that leverages custom feature engineering and quasi-random hypervector encoding to further enhance the efficiency and accuracy of HDC for AMS detection. The proposed framework demonstrates the potential for seamless integration into wearable biomedical devices for on-the-go health monitoring.
Language
English
Publication Title
The 2025 IEEE International Symposium on Circuits and Systems (ISCAS)
Rights
This is a peer reviewed Accepted Manuscript version of this article and is available through CWRU's Faculty Open Access Policy
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Recommended Citation
Abu Kaisar Mohammad Masum, Reeti Pradhananga, Jonas I Schmidt, Mehran Shoushtari Moghadam, M. Hassan Najafi, Bige Unluturk, Ulkuhan Guler, and Sercan Aygun, “AMS-HD: Acute Mountain Sickness Detection with Hyperdimensional Computing,” The 2025 IEEE International Symposium on Circuits and Systems (ISCAS), May 2025.