Human mobility is an important risk factor affecting disease transmission. Therefore, understanding detailed spatial behaviors and interactions among individuals is a fundamental issue. Past studies using high-resolution human contacts data from smart phones with GPS logs have captured spatial-temporal heterogeneity and daily contact patterns among individuals. However, measuring personalized exposed risk of infectious disease transmission is still under development. The purpose of the study is to establish a location-based framework for assessing personalized exposed risk of infectious disease transmission. The framework consists of three components: the first is client-side smart phone-based risk assessment module. We developed Android application for collecting real-time location data and displaying the personalized exposed risk score. The second component is the server-side epidemic simulation model. The simulation model calculated the personalized exposed risk score based on real-time GPS logs and individual mobility data from the client-side Android application. The last component is the disease alarm device for triggering the service-side epidemic simulation model. We installed infrared sensors in people-gathering areas as the alarm device to monitor human body temperature for detecting fever syndrome. We used NTU main campus as a pilot study to demonstrate the feasibility of the framework. We analyzed the records of students’ taking course and modeled the spatial interaction relationships among classroom buildings due to students’ mobility around the campus. Someone who got a fever is detected by the sensor and the server-side epidemic simulation is triggered. Each student who installed the client-side risk assessment module in his/her smart phone receives the real-time personalized exposed risk score when an epidemic outbreak on the NTU campus. The study proposed a location-based framework for measuring real-time personalized exposed risk. Each student at the campus could understand the spatial diffusion of disease transmission and make better spatial decisions based on personalized exposed risk scores.