近年來，心臟疾病的患者年齡逐年下降，而死亡率最高的心肌梗塞的前兆多為心肌缺血。傳統上的心臟疾病檢測方法無法提供醫療照護者一套即時且有效的監測方式，造成無法適時進行醫療措施。本論文提出一種可即時AR視覺化方式監測場域中人員生理狀況，在穿戴式裝置上直接進行特徵的計算與心電訊號判讀，考慮到RGB攝影機有隱私權的爭議，結合RPLiDAR與慣性穿戴裝置之身分辨識系統，可以對場域內人員進行身份辨識與追蹤，接著透過AR視覺化技術於監控畫面上即時顯示每位受照護人員的生理狀況，每位人員的生理感測資訊即時地被紀錄與執行特徵分析。透過機器學習模型判斷心肌缺血與基於感測融合之身份辨識技術，當受照護者的生理狀況異常的時，會即時呈現於監控視窗，協助醫療照護者預防緊急狀況。根據實驗結果，本系統所開發的演算法較其他演算法準確且具效率。

In recent year, the age of patients with heart disease has been decreasing year by year. Myocardial ischemia is the symptom of myocardial infarction, which has the highest mortality rate among heart diseases. Traditional methods can-not provide medical caregivers with a set of immediate and effective monitoring methods, which can hardly provide immediate medical measurement. As a result, we propose an instant AR visualization method to monitor the physiological condition of people in these field to develop the calculation of features and the interpretation of cardiac signals on wearable de-vice. Considering the controversy over the privacy of RGB cameras, we combine RPLiDAR to develop an inertial wearable device identity recognition system, which can identify and track the people in the field. Each person's physio-logical sensory information is instantly recorded and characterized, which makes medical care-givers monitor at any time clearly and quickly. Through using machine learning model to determine myocardial ischemia and sensor-based fusion of identity recognition technology, it will actively issue alerts when the care recipient's physiological condition is abnormal and to assist healthcare provider in preventing possible emergency situations. In the experimental evaluation, the algorithms developed in this system are more accurate and efficient than other algorithms.