Background: Cuff-based blood pressure measurement lacks comfort and convenience. Here, we examined whether blood pressure can be determined in a contactless manner using a novel smartphone-based technology called transdermal optical imaging. This technology processes imperceptible facial blood flow changes from videos captured with a smartphone camera and uses advanced machine learning to determine blood pressure from the captured signal. Methods: We enrolled 1328 normotensive adults in our study. We used an advanced machine learning algorithm to create computational models that predict reference systolic, diastolic, and pulse pressure from facial blood flow data. We used 70% of our data set to train these models and 15% of our data set to test them. The remaining 15% of the sample was used to validate model performance. Results: We found that our models predicted blood pressure with a measurement bias±SD of 0.39±7.30 mm Hg for systolic pressure, −0.20±6.00 mm Hg for diastolic pressure, and 0.52±6.42 mm Hg for pulse pressure, respectively. Conclusions: Our results in normotensive adults fall within 5±8 mm Hg of reference measurements. Future work will determine whether these models meet the clinically accepted accuracy threshold of 5±8 mm Hg when tested on a full range of blood pressures according to international accuracy standards.

Perceptual learning substantially improves visual discrimination and detection ability, which has been associated with visual cortical plasticity. However, little is known about the dynamic changes in neuronal response properties over the course of training. Using chronically implanted multielectrode arrays, we were able to capture day-by-day spatiotemporal dynamics of neurons in the primary visual cortex (V1) of monkeys trained to detect camouflaged visual contours. We found progressive strengthening and accelerating in both facilitation of neurons encoding the contour elements and suppression of neurons responding to the background components. The enhancement of this figure-ground contrast in V1 was closely correlated with improved behavioral performance on a daily basis. Decoding accuracy of a simple linear classifier based on V1 population responses also paralleled the animal's behavioral changes. Our results indicate that perceptual learning shapes the V1 population code to allow a more efficient readout of task-relevant information.

No abstract

The present study examined the validity of a novel physiological measurement technology called transdermal optical imaging (TOI) technology at assessing basal stress. This technology conveniently, contactlessly, and remotely measures facial blood flow changes using a conventional digital video camera. We compared data from TOI against the pulse data collected from the FDA approved BIOPAC system. One hundred thirty-six healthy adults participated in the study. We found that TOI measurements of heart rate and heart rate variability (HRV), which reflects basal stress, corresponded strongly to those obtained from BIOPAC. These findings indicate that TOI technology is a viable method to monitor heart rate and HRV not only accurately but also conveniently, contactlessly, and remotely. Further, measures of HRV obtained via TOI serves as a valid index of basal stress. Potential applications of this technology in psychological research and other fields are discussed.