Assessing hemodynamics from the photoplethysmogram to gain insights into vascular age: A review from VascAgeNet

The photoplethysmogram (PPG) signal is widely measured by clinical and consumer devices, and it is emerging as a potential tool for assessing vascular age. The shape and timing of the PPG pulse wave are both influenced by normal vascular ageing, changes in arterial stiffness and blood pressure, and atherosclerosis. This review summarises research into assessing vascular age from the PPG. Three categories of approaches are described: (i) those which use a single PPG signal (based on pulse wave analysis); (ii) those which use multiple PPG signals (such as pulse transit time measurement); and (iii) those which use PPG and other signals (such as pulse arrival time measurement). Evidence is then presented on the performance, repeatability and reproducibility, and clinical utility of PPG-derived parameters of vascular age. Finally, the review outlines key directions for future research to realise the full potential of photoplethysmography for assessing vascular age.

Effects of Contact Pressure in Reflectance Photoplethysmography in an In Vitro Tissue-Vessel Phantom

With the continued development and rapid growth of wearable technologies, PPG has become increasingly common in everyday consumer devices such as smartphones and watches. There is, however, minimal knowledge on the effect of the contact pressure exerted by the sensor device on the PPG signal and how it might affect its morphology and the parameters being calculated. This study explores a controlled in vitro study to investigate the effect of continually applied contact pressure on PPG signals (signal-to-noise ratio (SNR) and 17 morphological PPG features) from an artificial tissue-vessel phantom across a range of simulated blood pressure values. This experiment confirmed that for reflectance PPG signal measurements for a given anatomical model, there exists an optimum sensor contact pressure (between 35.1 mmHg and 48.1 mmHg). Statistical analysis shows that temporal morphological features are less affected by contact pressure, lending credit to the hypothesis that for some physiological parameters, such as heart rate and respiration rate, the contact pressure of the sensor is of little significance, whereas the amplitude and geometric features can show significant change, and care must be taken when using morphological analysis for parameters such as SpO2 and assessing autonomic responses.

Online tools to easily build augmented reality models of biomolecules that run right on the web

Several groups developed in the last years augmented and virtual reality (AR/VR) programs and apps to visualize 3D molecules, most rather static, limited in content, and requiring software installs, some even requiring expensive hardware. During the Covid-19 pandemic, our team launched moleculARweb (https://molecularweb.epfl.ch), a website that offers interactive content for chemistry and structural biology education through commodity web-based AR that works on consumer devices like smartphones, tablets and laptops. Among thousands of users, teachers increasingly request more biological macromolecules to be available, a demand that we cannot satisfy individually. Therefore, to allow users to build their own material, we built a web interface where any user can build any online AR experience in few steps starting from a PDB structure or from virtual objects/scenes exported from VMD. The website also returns a WebXR session for viewing and manipulating the model in high-end immersive VR headsets with web browsers, here tested on the ~400 USD Oculus Quest 2. The tool is accessible at https://molecularweb.epfl.ch/pages/pdb2ar.html.

Online tools to easily build augmented reality models of biomolecules that run right on the web

Several groups developed in the last years augmented and virtual reality (AR/VR) programs and apps to visualize 3D molecules, most rather static, limited in content, and requiring software installs, some even requiring specialized hardware. During the Covid-19 pandemic, our team launched moleculARweb (https://molecularweb.epfl.ch), a website that offers interactive content for chemistry and structural biology education through web-based AR that works on consumer devices like smartphones, tablets and laptops. The website quickly got thousands of student and teacher users, a substantial fraction of them accessing from their homes given the pandemic. Teachers have been increasingly requesting more biological macromolecules to be available in AR, a demand that we cannot satisfy individually. Therefore, to allow them to build their own material, and also to help us expedite development of activities, we built a web interface where any user can build any online AR experience in few steps starting from a PDB structure or from virtual objects/scenes exported from VMD. We here briefly describe the tool, that is accessible at https://molecularweb.epfl.ch/pages/pdb2ar.html.

Polypyrrole and Graphene Nanoplatelets Inks as Electrodes for Flexible Solid-State Supercapacitor

Flexible energy storage devices and supercapacitors in particular have become very attractive due to the growing demand for wearable consumer devices. To obtain supercapacitors with improved performance, it is useful to resort to hybrid electrodes, usually nanocomposites, that combine the excellent charge transport properties and high surface area of nanostructured carbon with the electrochemical activity of suitable metal oxides or conjugated polymers. In this work, electrochemically active conducting inks are developed starting from commercially available polypyrrole and graphene nanoplatelets blended with dodecylbenzenesulfonic acid. Films prepared by applying the developed inks are characterized by means of Raman measurements, Fourier Transform Infrared (FTIR) analysis, and Atomic Force Microscopy (AFM) investigations. Planar supercapacitor prototypes with an active area below ten mm2 are then prepared by applying the inks onto transparency sheets, separated by an ion-permeable nafion layer impregnated with lithium hexafluorophospate, and characterized by means of electrical measurements. According to the experimental results, the devices show both pseudocapacitive and electric double layer behavior, resulting in areal capacitance that, when obtained from about 100 mFcm−2 in the sample with polypyrrole-based electrodes, increases by a factor of about 3 when using electrodes deposited from inks containing polypyrrole and graphene nanoplateles.

Preprocessing for Unintended Conducted Emissions Classification with ResNet

Characterization of Unintended Conducted Emissions (UCE) from electronic devices is important when diagnosing electromagnetic interference, performing nonintrusive load monitoring (NILM) of power systems, and monitoring electronic device health, among other applications. Prior work has demonstrated that UCE analysis can serve as a diagnostic tool for energy efficiency investigations and detailed load analysis. While explaining the feature selection of deep networks with certainty is often not fully comprehensive, or in other applications, quite lacking, additional tools/methods for further corroboration and confirmation can help further the understanding of the researcher. This is true especially in the subject application of the study in this paper. Often the focus of such efforts is the selected features themselves, and there is not as much understanding gained about the noise in the collected data. If selected feature and noise characteristics are known, it can be used to further shape the design of the deep network or associated preprocessing. This is additionally difficult when the available data are limited, as in the case which the authors investigated in this study. Here, the authors present a novel work (which is a proposed complementary portion of the overall solution to the deep network classification explainability problem for this application) by applying a systematic progression of preprocessing and a deep neural network (ResNet architecture) to classify UCE data obtained via current transformers. By using a methodical application of preprocessing techniques prior to a deep classifier, hypotheses can be produced concerning what features the deep network deems important relative to what it perceives as noise. For instance, it is hypothesized in this particular study as a result of execution of the proposed method and periodic inspection of the classifier output that the UCE spectral features are relatively close to each other or to the interferers, as systematically reducing the beta parameter of the Kaiser window produced progressively better classification performance, but only to a point, as going below the Beta of eight produced decreased classifier performance, as well as the hypothesis that further spectral feature resolution was not as important to the classifier as rejection of the leakage from a spectrally distant interference. This can be very important in unpredictable low-FNR applications, where knowing the difference between features and noise is difficult. As a side-benefit, much was learned regarding the best preprocessing to use with the selected deep network for the UCE collected from these low power consumer devices obtained via current transformers. Baseline rectangular windowed FFT preprocessing provided a 62% classification increase versus using raw samples. After performing a more optimal preprocessing, more than 90% classification accuracy was achieved across 18 low-power consumer devices for scenarios in which the in-band features-to-noise ratio (FNR) was very poor.

Smartphone display size can influence perceptual judgments

Classic research in perception has suggested that visual context can impact how individuals perceive object characteristics like physical size. The current set of studies extends this work to an applied setting by examining whether smartphone display size can impact the perception of objects presented on smartphones. Participants viewed several target items, on two different sized virtual device displays based on actual consumer devices and were asked to make simple judgments of the size of presented objects. Results from both experiments confirm that display size impacts perceived size, such that larger displays cause users to significantly underestimate the size of objects moreso than smaller displays. This is the first study to confirm such an effect, and suggests that beyond aesthetics or cost, one’s personal choice of device might have additional performance consequences.