###### Abstract  
The COVID-19 pandemic has underscored the necessity for rapid contact tracing as a means to effectively suppress the spread of infectious diseases. Existing contact tracing methods leverage location-based or distance-based detection to identify contact with a confirmed patient. Existing contact tracing methods have encountered challenges in practical applications, stemming from the tendency to classify even casual contacts, which carry a low risk of infection, as close contacts. This issue arises because the transmission characteristics of the virus have not been fully considered. This study addresses the above problem by proposing IntelliTrace, an intelligent method that introduces methodological innovations prioritizing shared environmental context over physical proximity. This approach more accurately assesses potential transmission events by considering the transmission characteristics of the virus, with a special focus on COVID-19. In this study, we present space-based indoor Wi-Fi contact tracing using machine learning for indoor environments and trajectory-based outdoor GPS contact tracing for outdoor environments. For an indoor environment, a contact is detected based on whether users are in the same space with the confirmed case. For an outdoor environment, we detect contact through judgments based on the companion statuses of people, such as the same movements in their trajectories. The datasets obtained from 28 participants who installed the smartphone application during a one-month experiment in a campus space were utilized to train and validate the performance of the proposed exposure-detection method. As a result of the experiment, IntelliTrace exhibited an F1 score performance of 86.84% in indoor environments and 94.94% in outdoor environments. contact tracing; Wi-Fi fingerprint; GPS; COVID-19 20232020231/1/20232020231/1/20232020231/1/2023202020231/1/202320  
fingerprint [6] are representative location-based methods that analyze the location information of a confirmed case to determine the contact status. However, since location-based contact detection methods require the use of personal information, privacy issues have been raised. Therefore, it is difficult to apply these methods in many countries. To address this problem, methods using Bluetooth for distance-based contract detection instead of location-based information have been developed [7]. The Google Apple Exposure Notification (GAEN) [8; 9], which was jointly developed by Google and Apple, is a distance measurement-based contact tracing method that uses Bluetooth signals. After exchanging tokens between users of GAEN, when a confirmed case occurs, the users who exchanged tokens are notified of their contact status. GAEN can protect personal information and inform contacts in real time. However, the GAEN method faces the following challenges for large-scale adoption and application. The first problem is related to the radio frequency (RF) signal strength accuracy of Bluetooth, which is used by GAEN. [PERSON] and [PERSON] [10; 11] conducted a GAEN API measurement-based evaluation on commuters and buses in Dublin, and their results indicated that there was a low correlation between the Bluetooth-received signal strength and the physical distance among the devices installed with the app. In addition, in an experiment related to detecting proximity contact using the GAEN API-based COVID-19 contact tracing app, approximately half of the device pairs were not judged to exposure despite being less than 2 m apart. These previous studies show that the accuracy of GAEN API-based contact tracing is not high for practical applications because of the accuracy limitations of the Bluetooth radio signal. The second problem is that GAEN simply determines the contact based on the distance between people without considering the transmission characteristics of the SARS-CoV-2 virus. Therefore, more contacts are traced than necessary, which generates overly frequent contact cases.