Background: Measuring heart rate variability (HRV) through wearable photoplethysmography sensors from smartwatches is gaining popularity for monitoring many health conditions. However, missing data caused by insufficient wear compliance or signal quality can degrade the performance of health metrics or algorithm calculations. Research is needed on how to best account for missing data and to assess the accuracy of metrics derived from photoplethysmography sensors.
View Article and Find Full Text PDFBackground: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information.
Objective: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19.
Background: The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions.
Methods: Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors. This framework allowed for responses to be accurately referenced to the infection event.
The reach and impact of the Internet of Things will depend on the availability of low-cost, smart sensors-"low cost" for ubiquitous presence, and "smart" for connectivity and autonomy. By using wafer-level processes not only for the smart sensor fabrication and integration, but also for packaging, we can further greatly reduce the cost of sensor components and systems as well as further decrease their size and weight. This paper reviews the state-of-the-art in the wafer-level vacuum packaging technology of smart sensors.
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