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 SpO$_{\bf 2}$ 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 SpO$_{\bf 2}$ 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.