## 6 Conclusions  
As the COVID-19 pandemic continues, there has been a surge in research focused on contact tracing. We introduced IntelliTrace, a novel contact tracing method that incorporates co-location--being in the same space or same trajectory--with the consideration of the transmission characteristics of the virus. We employed a space-based contact tracing method using Wi-Fi for indoor spaces and a trajectory-based contact tracing method using GPS for outdoor spaces. The indoor Wi-Fi contact tracing method utilized values of Euclidean, Cosine, and Jaccard similarities derived from the fingerprint data of Wi-Fi access points as features. Furthermore, machine learning models were employed to detect exposure by making decisions regarding the co-location of confirmed cases and potential contacts in the same space. The outdoor GPS contact tracing method relied on calculating distances between individuals and similarities between trajectory vectors using GPS data. Next, we evaluated the performance of our proposed method using a validation dataset, which was gathered over a one-month experiment involving 28 participants. IntelliTrace demonstrated an F1 score performance of 86.84% in indoor environments and 94.94% in outdoor environments. Through comparative evaluations, we have substantiated the efficacy of our approach, demonstrating its superior performance in relation to established methods. The proposed algorithm is expected to contribute to digital means of containing the spread of infectious diseases in the future, especially when combined with other methods such as distributed data storage and anonymization of personal information.  
[PERSON] and [PERSON]-H.K. proposed the idea for this study and developed the concept. The data and scenario design for the experiment were prepared by [PERSON]-R.J. and [PERSON]. All authors performed the analysis of the study. The first draft of the manuscript was written by [PERSON] and [PERSON]-H.K. The review process, supervision, and approval of the final manuscript were carried out by [PERSON] All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GAEN [28]** & \multicolumn{3}{c}{**Our Study**} \\ \hline Precision & 70.00 & Indoor & Outdoor & Overall \\ \cline{2-5}  & 96.68 & 96.31 & 96.49 \\ \hline Recall & 47.00 & 78.82 & 96.60 & 87.71 \\ \hline Accuracy & 79.00 & 88.41 & 97.50 & 92.95 \\ \hline F1 Score & 56.24 & 86.84 & 94.94 & 90.89 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative performance metrics of our study against contact tracing using GAEN [28].  
**Funding:** This research was partly supported by the Ministry of Science and ICT (MSIT), Korea, under the Information Technology Research Center (ITRC) support program (IITP-2023-2021-0-01816) supervised by the Institute for Information & Communications Technology Planning & Evaluation (IITP), by a grant of the project for The Government-wide R&D to Advance Infectious Disease Prevention and Control, Republic of Korea (grant number: HG23C0003), and by Institute of Information & communications Technology Planning & Evaluation (IITP) under the metaverse support program to nurture the best talents (IITP-2023-RS-2023-00254529) grant funded by the Korea government(MSIT).  
**Institutional Review Board Statement:** All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Sejong University (SU-2021-006, 30 August 2021).  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data used in the experiment cannot be disclosed without the consent of the participants.  
**Conflicts of Interest:** The authors declare no conflict of interest.