## 4 Experimental Design  
### Data Collection  
* Data gathering  
We developed a smartphone application for data collection and utilized it for data gathering. The boundary of the Sejong University campus was established using geofence technology, which creates a virtual perimeter based on location information, since participants were involved in the experiment. To prevent the excessive collection of personal information, data were gathered only when the users' real-time locations were detected within the geofence, and data collection was discontinued when the users moved out of the geofence. The detailed items of the data collected through the data-gathering application are as follows: LOG TIME, SSID, BSSID, and \(RSSI\) for Wi-Fi; LOG TIME, DEVICE NAME, and \(RSSI\) for BLE Beacon; LOG TIME, altitude, latitude, longitude, and provider for GPS. Figure 5 shows the main screen of the data-gathering application used in the experiment. Participant recruitment within the university institution was conducted via an online portal. We set the number of participants to 28, taking into account ethical issues and the need for smooth and accurate management of the participants. The cohort consisted of 22 students and 6 staff members, with a balanced gender distribution. Comprehensive explanations of the experimental procedures were provided, and all participants gave their informed consent prior to the commencement of the study. For data collection, participants were equipped with mobile phones and were instructed to use pseudonyms in place of their real names to safeguard their privacy and ensure the anonymity of the collected data. Participants were required to consistently carry their mobile phones to maintain data integrity. Regarding research ethics, the experiment was conducted in strict accordance with the ethical guidelines set forth by our institution's Research Ethics Board. Additionally, the confidentiality of participant data was prioritized, and measures were implemented to prevent unauthorized access to sensitive information. During the course of conducting this experiment, the scenario was not conducted with pre-confirmed patients designated in advance. Following the scenario, 'confirmed patient' were assigned to data randomly to maintain the integrity of the study's realism. Furthermore, data received from the app utilized pseudonyms and unique user identifiers (UIDs) rather than real names. Because of this design, participants were unaware of each other's behavior and did not modify their own behavior. We provided participants with a small stipend to offset any expenses or inconveniences incurred during the experiment. Figure 4: Exposed case detection process of trajectory-based outdoor GPS contact tracing. * Training dataset  
We created training datasets according to the scenario to increase the accuracy of training through precise labeling. The training data were collected from 10 individuals during a scenario in which their movements and exposure statuses were pre-determined. During the experiment, each participant (10) took turns acting as the confirmed patient, while the remaining 9 participants were considered potential contacts, thereby augmenting the dataset. This design enabled the collection of a diverse dataset, simulating various contact scenarios and providing a foundation for training our contact tracing model. Whenever the participants visited these spaces, the BLE data of the beacon installed in places were simultaneously collected in the data-gathering application on participants' smartphones. The collected data were used to confirm the accuracy of the collected datasets and train the machine learning model for the proposed contact tracing method. Using the data-gathering application, a total of 300,645 fingerprints of Wi-Fi APs and 200,000 GPS data points were collected for the training dataset. * Validation dataset  
For the experiment, we obtained consent from 28 participants, consisting of Sejong University students and faculty members.