## 5 Conclusions  
Ubiquitous healthcare systems have attracted a great deal of attention in recent years for their application in numerous fields such as e-health, telemedicine and home nursing. The enormous amount of research in this area motivates us to present this work. In this paper, a smart ubiquitous healthcare monitoring architecture was developed that was capable of handling the healthcare process from the gathering of bio-sensed data to the management of critical information for effective administration of healthcare monitoring in order to provide services. In this study, we used WBAN sensors to gather vital patient information, and then transmitted this information to be separated into different dataset categories. Statistical calculations were carried out with the aim of efficiently classifying abnormal physiological values (blood pressure application). Taken together, these results provided additional evidence that ubiquitous smart systems could be efficiently applied to the Internet of Things with the use of smart technologies that were capable of communicating medical data to be analyzed. The results of this work appeared to validate the proposed model. This study was not specifically designed to evaluate factors related to security or scalability, or to provide data that are missing due to gathering and communication issues. One of the major drawbacks of this work was that of finding medical data sources that naturally fit a probabilistic approach. Blood pressure readings are known to fit this hypothesis, and this was confirmed using various normality tests as described above. Future work will focus on combining learning techniques with a software-defined networking system while focusing on data behavior in critical boundaries related to the application of the normal distribution to generate new insights. Conceptualization, R.C. and S.R. ; methodology, R.C. and S.R. ; software, R.C. and S.R. ; validation, R.C. and S.R. ; formal analysis, R.C. and S.R. ; investigation, R.C. and S.R. ; resources, R.C. and S.R. ; data curation, R.C. and S.R. ; writing--original draft preparation, R.C. and S.R. ; writing--review and editing, R.C. and S.R. ; visualization, R.C. and S.R. ; supervision, R.C. and S.R. ; project administration, R.C. and S.R. This research received no external funding. The authors declare no conflict of interest. The following abbreviations are used in this manuscript:  
WBAN: Wireless Body Area Networks  
SYS: Systolic  
DIAS: Diastolic  
SSS: Smart Surveillance  
DHS: Digital Health System  
PDS: Patient Data Store  
DA: Data Analytics  
Q-Q: Quantile-Quantile  
SVM: Support Vector Machine  
RBF: Radial Basis Function  
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