## 6 Summary and Outlook  
We presented a modular architecture that is designed to collect and analyze minimally processed data from wearable devices. Because the system was designed to be standalone and independent of OEM cloud services, its software stack incorporates routines for end-to-end data processing, including data cleaning, metrics extraction, and metrics standardization. When implemented using a local server, data acquisition and data processing are achieved with no connection to the Internet; raw data, metrics, and algorithm results can later be transmitted to designated cloud resources when connectivity is established.  
We deployed this system with Garmin Fenix series smartwatches and used smartphones or tablets running the S+ app to acquire high-resolution data. The S+ app transferred the data to either to a local server (e.g., laptop or single-board computer) or a cloud resource (e.g., AWS cloud clusters) where data were stored and analyzed. The system is currently being implemented in conjunction with the S+ algorithm for detecting viral respiratory illness before symptom onset and the algorithm for estimating core body temperature from average HR [17; 37; 38].  
The proposed architecture has the potential to broaden the use of smartwatches in applications such as monitoring military personnel or first responders where communication resources may be limited, and strict security standards are in place. This system is also useful for research studies, where access to high-resolution data can be leveraged to develop and improve the accuracy and interpretability of custom algorithms and analytical models for disease diagnostics and human performance assessment. Future development efforts will be focused on increasing the number of wearables that can be used with the platform, as well as implementing additional algorithms and metrics that target short-term health changes due to exertional heat illness, psychological stress, and cognitive fatigue. The long-term concept is to provide a wearable sensor system that provides capabilities ranging from fitness and performance monitoring to remote, near real-time medical care.  
Conceptualization, D.S.T. and D.E.D.; methodology, D.S.T., M.H.-C. and M.A.B.; software, M.D.B., M.A.B. and R.P.E.; formal analysis, M.H.-C., P.G., J.R.H. and H.D.-W.; data curation, E.A.P.; writing--original draft preparation, D.S.T., M.H.-C. and P.G., writing--review and editing, M.A.B., D.E.D. and H.J.W.; visualization, P.G.; validation, H.J.W., project administration, D.E.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Defense Advanced Research Projects Agency (DARPA), contract no. 140D63-19-C-0023. The views, opinions, and/or findings expressed are those of the author(s) and should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.  
Data Availability StatementData are not publicly available due to privacy and legal restrictions.  
The authors gratefully acknowledge valuable technical discussions with [PERSON] and [PERSON] of DARPA.  
The authors declare no conflict of interest.