IoT-Enabled Intelligent Dynamic Risk Assessment of Acute Mountain Sickness: The Role of Event-Triggered Signal Processing

The rapid developments in Internet of Medical Things open up new avenues for personalized healthcare. Continuously monitored physiological data can be collected by wearable devices and are transmitted to a remote server for real-time monitoring and diagnosis. This article concerns a risk assessment problem of acute mountain sickness (AMS) with data transmitted according to an event-triggered transmission schedule. An event-triggered signal processing approach is introduced to reconstruct the untransmitted information, based on which, a dynamic SpObf 2 index (DSI) is further proposed for AMS risk evaluation. The performance of the proposed approach is analyzed through physiological data collected in a proof-of-the-concept study (N=12). Statistical significant correlation of the DSI with AMS ground truth including Lake Louise score, deep sleep duration, deep sleep ratio, and mean SpObf 2 during sleep is observed. More importantly, it is observed that the proposed event-triggered signal processing procedure can dramatically reduce the data transmission rate while maintaining the performance of the DSI assessment, through comparison of the DSI obtained using the proposed event-triggered approach with those obtained based on event-triggered raw data and continuously transmitted time-triggered data. The obtained results indicate the feasibility of adopting event-triggered data scheduling and signal processing to achieve AMS risk evaluation using data from wearable devices with limited communication/battery resources.