Future Smart and Connected Communities (SCC) will utilize distributed sensors and embedded computing to seamlessly generate meaningful data that can assist individuals, communities, and society with interlocking physical, social, behavioral, economic, and infrastructural interaction. SCC will require newer technologies for seamless and unobtrusive sensing and computation in natural settings. This work presents a new technology for health monitoring with low-cost body-worn disposable fully passive electronic sensors, along with a scanner, smartphone app, and web-server for a complete smart sensor system framework. The novel wireless resistive analog passive (WRAP) sensors are printed using an inkjet printing (IJP) technique on paper with silver inks (Novacentrix Ag B40, sheet resistance of 21 mΩ/sq) and incorporate a few discrete surface mounted electronic components (overall thickness of <1 mm). These zero-power flexible sensors are powered through a wireless inductive link from a low-power scanner (500 mW during scanning burst of 100 ms) by amplitude modulation at the carrier signal of 13.56 MHz. While development of various WRAP sensors is ongoing, this paper describes development of a WRAP temperature sensor in detail as an illustration. The prototypes were functionally verified at various temperatures with energy consumption of as low as 50 mJ per scan. The data is analyzed with a smartphone app that computes severity (Events-of-Interest, or EoI) using a real-time algorithm. The severity can then be anonymously shared with a custom web-server, and visualized either in temporal or spatial domains. This research aims to reduce ER visits of patients by enabling self-monitoring, thereby improving community health for SSC.

Due to rapid development of wearable technologies used for health monitoring, a robust data reliability assessment technique is required. Choosing the right sets of Data reliability metrics (DReM) can improve the performance of data reliability assessment and thus, it can help in reliable data acquisitions of bio-signals. Traditional reliability assessment techniques rely significantly on the signal specific peak detection algorithms. It impedes the endeavor of generalizing signal independent data reliability assessment techniques. In this work, we explored nine signal independent statistical candidates for DReM and finalized five top features as our DReMs which can identify acceptable signal segments from unacceptable segments with 0.83 precision, 0.84 recall and 0.83 F-1 score on accurately identifying acceptable pulses (ECG, PPG and respiratory signal). Additionally, the five DReMs are capable of detecting unacceptable signal segments with 0.92 precision, 0.92 recall and 0.92 F-1 score. We proposed optimal Random Forest classifier model with excellent Receiver Operating Characteristics (ROC) with significant Area Under the Curve (AUC) value of 0.994.