Chemically Recyclable, High Molar Mass Polyoxazolidinones via Ring-Opening Metathesis Polymerization.

The development of robust methods for the synthesis of chemically recyclable polymers with tunable properties is necessary for the design of next-generation materials. Polyoxazolidinones (POxa), polymers with five-membered urethanes in their backbones, are an attractive target because they are strongly polar and have high thermal stability, but existing step-growth syntheses limit molar masses and methods to chemically recycle POxa to monomer are rare. Herein, we report the synthesis of high molar mass POxa via ring-opening metathesis polymerization of oxazolidinone-fused cyclooctenes. These novel polymers show <5% mass loss up to 382-411 °C and have tunable glass transition temperatures (14-48 °C) controlled by the side chain structure. We demonstrate facile chemical recycling to monomer and repolymerization despite moderately high monomer ring-strain energies, which we hypothesize are facilitated by the conformational restriction introduced by the fused oxazolidinone ring. This method represents the first chain growth synthesis of POxa and provides a versatile platform for the study and application of this emerging subclass of polyurethanes.

Quantification of Active Visual Attention using RGB camera.

Active visual attention (AVA) is the cognitive ability that helps to focus on important visual information while responding to a stimulus and is important for human-behavior and psychophysiological research. Existing eye-trackers/camera-based methods are either expensive or impose privacy issues as face videos are recorded for analysis. Proposed approach using blink-rate variability (BRV), is inexpensive, easy to implement, efficient and handles privacy issues, making it amenable to real-time applications. Our solution uses laptop camera/webcams and a single blink feature, namely BRV. First, we estimated participant's head pose to check camera alignment and detect if he is looking at the screen. Next, subject-specific threshold is computed using eye aspect ratio (EAR) to detect blinks from which BRV signal is constructed. Only EAR values are saved, and participant's face video is NOT saved or transmitted. Finally, a novel AVA score is computed. Results shows that the proposed score is robust across participants, ambient light conditions and occlusions like spectacles.

MedTric : A clinically applicable metric for evaluation of multi-label computational diagnostic systems.

When judging the quality of a computational system for a pathological screening task, several factors seem to be important, like sensitivity, specificity, accuracy, etc. With machine learning based approaches showing promise in the multi-label paradigm, they are being widely adopted to diagnostics and digital therapeutics. Metrics are usually borrowed from machine learning literature, and the current consensus is to report results on a diverse set of metrics. It is infeasible to compare efficacy of computational systems which have been evaluated on different sets of metrics. From a diagnostic utility standpoint, the current metrics themselves are far from perfect, often biased by prevalence of negative samples or other statistical factors and importantly, they are designed to evaluate general purpose machine learning tasks. In this paper we outline the various parameters that are important in constructing a clinical metric aligned with diagnostic practice, and demonstrate their incompatibility with existing metrics. We propose a new metric, MedTric that takes into account several factors that are of clinical importance. MedTric is built from the ground up keeping in mind the unique context of computational diagnostics and the principle of risk minimization, penalizing missed diagnosis more harshly than over-diagnosis. MedTric is a unified metric for medical or pathological screening system evaluation. We compare this metric against other widely used metrics and demonstrate how our system outperforms them in key areas of medical relevance.

LTH-ECG: Lottery Ticket Hypothesis-based Deep Learning Model Compression for Atrial Fibrillation Detection from Single Lead ECG On Wearable and Implantable Devices.

Atrial Fibrillation (AF) is a kind of arrhythmia, which is a major morbidity factor, and AF can lead to stroke, heart failure and other cardiovascular complications. Electrocardiogram (ECG) is the basic marker to test the condition of heart and it can effectively detect AF condition. Single lead ECG has the practical advantage for being small form factor and it is easy to deploy. With the sophistication of the current deep learning (DL) models, researchers have been able to construct cardiologist-level models to detect different arrhythmias including AF condition detection from single lead short-time ECG signals. However, such models are computationally expensive and require huge memory size for deployment (more than 100 MB to deploy state-of-the-art 34-layer convolutional neural network-based ECG classification model). Such models need to be significantly trimmed with insignificantly loss of its classification performance for deployment in practical applications like single lead ECG classification in wearable and implantable devices. We have found that classical deep learning model compression techniques like pruning, quantization are not capable of substantial model size reduction without compromising on the model performance. In this paper, we propose LTH-ECG, which is our novel goal-driven winning lottery ticket discovery method, where lottery ticket hypothesis (LTH)-based iterative model pruning is used with the aim of over-pruning avoidance. LTH-ECG reduces the model size by 142x times with insignificant loss of classification performance (less than 1 % test F1-score penalty). Clinical Relevance- LTH-ECG will enable practical deployment for remote screening of AF condition using single lead short-time ECG recordings such that patients can on-demand monitor AF condition remotely through wearable ECG sensing devices and report cardiological abnormality to the concerned physician. LTH-ECG acts as an early warning system for effective AF condition screening.

An approach to a wrist wearable based Covid-19 prediction system to protect Health Care Professionals.

With healthcare professionals being the frontline warriors in battling the Covid pandemic, their risk of exposure to the virus is extremely high. We present our experience in building a system, aimed at monitoring the physiology of these professionals 24/7, with the hope of providing timely detection of infection and thereby better care. We discuss a machine learning approach and model using signals from a wrist wearable device to predict infection at a very early stage. In a double-blind test on a small group of patients, our model could successfully identify the infected versus non-infected cases with near 100% accuracy. We also discuss some of the challenges we faced, both technical and non-technical, to get this system off the ground as well as offer some suggestions that could help tackle these hurdles.

Surface Potential Simulation and Electrode Design for in-Ear EEG Measurement.

The need for everyday-real-time EEG sensing has resulted in the development of minimalistic and discreet wearable EEG measuring devices. A front runner in this race is in-ear worn device. However, the position of the ear as well as its restricted accessibility poses certain challenges in the design of such devices - from the type of materials used to the placement of electrodes. These choices are important as they will determine the quality of the EEG signal available and in turn the accuracy of the application. We therefore create a simulation model of the human ear, allowing us to understand the impact of these choices on our design of an In-Ear EEG wearable. We first study the signal acquisition characteristics of a proposed gold-plated electrode against two other state-of-the-art electrode materials for in-ear EEG data collection. We further validate this electrode choice by fabricating a personalized silicone-based earpiece and collecting in-situ EEG data. This work explores the properties of using gold plated electrodes in capturing in-ear EEG signals enabling unobtrusive collection of the brain physiology data in real world setting.

Design of a Realtime Photoplethysmogram Signal Quality Checker for Wearables and Edge Computing.

Photoplethysmogram (PPG) signal is extensively used for deducing health parameters of patients in order to infer about physiological conditions of heart, blood pressure, respiratory patterns, and so on. Such analysis and estimations can be done accurately only on high quality PPG signals with very minimal artifacts. PPG signals collected from fitness grade and smart phone scenarios are prone to muscle artifacts and hence there is a need to assess the signal quality before using the signal. Although there are approaches available in the realm of machine learning and deep learning, they are computationally expensive and may not be suitable for a wearable or edge computing scenario. In this paper, we propose the design of a quality checker to check the quality of the signal that can be directly implemented on edge devices like smartwatch. The algorithm is tested on PPG data collected from wearable, ICU and medical grade devices. In the wearable scenario where the noise levels are very high, our algorithm has performed significantly better with a Fscore of over 0.92. Further we show that by applying the proposed quality checker, the accuracy of the computed heart rate from a smart phone PPG-application significantly improves.

Coupling of acceptor-substituted diazo compounds and tertiary thioamides: synthesis of enamino carbonyl compounds and their pharmacological evaluation.

This paper describes the synthesis of enamino carbonyl compounds by the copper(i)-catalyzed coupling of acceptor-substituted diazo compounds and tertiary thioamides. We plan to use this method to synthesize indolizidine (-)-237D analogs to find α6-selective antismoking agents. Therefore, we also performed in silico α6-nAchRs binding studies of selected products. Compounds with low root-mean-square deviation values showed more favorable binding free energies. We also report preliminary pharmacokinetic data on indolizidine (-)-237D and found it to have weak activity at CYP3A4. In addition, as enamino carbonyl compounds are also known for antimicrobial properties, we screened previously reported and new enamino carbonyl compounds for antibacterial, antimicrobial, and antifungal properties. Eleven compounds showed significant antimicrobial activities.

Resource Constrained CVD Classification Using Single Lead ECG On Wearable and Implantable Devices.

Electrocardiogram (ECG) is one of the fundamental markers to detect different cardiovascular diseases (CVDs). Owing to the widespread availability of ECG sensors (single lead) as well as smartwatches with ECG recording capability, ECG classification using wearable devices to detect different CVDs has become a basic requirement for a smart healthcare ecosystem. In this paper, we propose a novel method of model compression with robust detection capability for CVDs from ECG signals such that the sophisticated and effective baseline deep neural network model can be optimized for the resource constrained micro-controller platform suitable for wearable devices while minimizing the performance loss. We employ knowledge distillation-based model compression approach where the baseline (teacher) deep neural network model is compressed to a TinyML (student) model using piecewise linear approximation. Our proposed ECG TinyML has achieved ~156x compression factor to suit to the requirement of 100KB memory availability for model deployment on wearable devices. The proposed model requires ~5782 times (estimated) less computational load than state-of-the-art residual neural network (ResNet) model with negligible performance loss (less than 1% loss in test accuracy, test sensitivity, test precision and test F1-score). We further feel that the small footprint model size of ECG TinyML (62.3 KB) can be suitably deployed in implantable devices including implantable loop recorder (ILR).

Affective response to tunes synthesized with musical pitch curves.

Tunes perceived as happy may help a user reach an affective state of positive valence. However, a user with negative valence may not be ready to listen to such a tune immediately. In this paper, we consider nudging a user from their current affective state to a target affective state in small steps. We propose a technique to generate a gradation of tunes between an initial-reference tune and a target-reference tune, to achieve the affect transition. The two-dimensional gradation is realized in time and in pitch, respectively, by varying the tempo and by the use of musical pitch curves, i.e. pitch transients or simply 'transients'. We exploit the duration and scaling of transients observed in South Indian music (Carnatic) to introduce transients into existing tunes. In our experiment, we have introduced the transients into Western music tunes. The results of perceptual evaluation show that the affective response to transients is likely to be higher at slow tempos than at fast tempos. Further, when felt, transient-tunes are twice as likely to be associated with positive valence than with negative valence, irrespective of tempo.

Single feature spatio-temporal architecture for EEG Based cognitive load assessment.

The study of electroencephalography (EEG) data for cognitive load analysis plays an important role in identification of stress-inducing tasks. This can be useful in applications such as optimal work allocation, increasing efficiency in the workplace and ensuring safety in difficult work environments. In order for such systems to be realistically deployable, easy acquisition and processing of the data on a wearable device is imperative. Current techniques primarily perform offline processing to analyse a multi-channel EEG to make a post facto assessment. This work focusses on building a new deep learning architecture that performs a single feature based spatio-temporal analysis of EEG data. This is achieved by creating a brain topographic map based on a single feature followed by spatio-temporal analysis using the developed network architecture. Data from two cognitive load experiments on the Physionet EEGMAT dataset were used to validate the performance. The network achieves an accuracy of 98.3% which is better than similar state-of-the-art approaches. Moreover, the proposed approach facilitates analysis of the spatial propagation of a signal, which is not possible through conventional EEG signal representations.

Cost Effective Real-time System for cognitive computing using Personalized Eye Blink Detection from Camera.

Eye blink is indicative of various mental states. Generally, vision based approaches are used for detecting eye blinks. However, performance of such approaches varies across participants. Standard eye tracker or eye glasses used for detecting blinks, are very costly. Here, we are proposing a personalized vision based eye blink detector system. Proposed approach is ubiquitous and unobtrusive in nature and can be implemented using standard webcams/mobile camera, making it deployable for real world scenarios. Our approach has been validated on a set of data collected from our lab and on an open data set. Results show that in both cases, our system performs well for various conditions like natural/artificial light, with or without spectacles. We achieved a Fscore of 0.98 for own collected data and 0.91 for open dataset, which outperform state of the art approaches.

A Lumped Parameter Model for Cardiac Output Estimation Using Arterial Blood Pressure Waveform.

This paper investigates a subject-specific lumped parameter cardiovascular model for estimating Cardiac Output (CO) using the radial Arterial Blood Pressure (ABP) waveform. The model integrates a simplified model of the left ventricle along with a linear third order model of the arterial tree and generates reasonably accurate ABP waveforms along with the Dicrotic Notch (DN). Non-linear least square optimization technique is used to obtain uncalibrated estimates of cardiovascular parameters. Thermodilution CO measurements have been used to evaluate the CO estimation accuracy. The model achieves less than 15% normalized error across 10 subjects with different shapes of ABP waveform.

Person and Stressor Independent Generic Model for Stress Detection Using GSR.

Stress detection is a widely researched topic and is important for overall well-being of an individual. Several approaches are used for prediction/classification of stress. Most of these approaches perform well for subject and activity specific scenarios as stress is highly subjective. So, it is difficult to create a generic model for stress prediction. Here, we have proposed an approach for creating a generic stress prediction model by utilizing knowledge from three different datasets. Proposed model has been validated using two open datasets as well as on a set of data collected in our lab. Results show that the proposed generic model performs well across studies conducted independently and hence can be used for monitoring stress in real life scenarios and to create mass-market stress prediction products.

Selective Discrimination of VOCs Applying Gas Sensing Kinetic Analysis over a Metal Oxide-Based Chemiresistive Gas Sensor.

Semiconducting metal oxide-based gas sensors have inadequate selectivity as they are responsive toward a variety of gases. Here, we report the implementation of gas sensing kinetic analysis of the sensor to identify the tested volatile organic compounds (VOCs) (2-propanol, formaldehyde, methanol, and toluene) precisely. A single chemiresistive sensor was employed having tin oxide-based hollow spheres as the sensing material, which were obtained by chemical synthesis. The gas sensing measurements were conducted in a dynamic manner where the sensor displayed excellent response with high sensitivity. Eley-Rideal model was adopted to obtain the kinetic properties of the gas sensing phenomenon through theoretical fitting of response transient curves and their corresponding kinetic parameters. The calculated characteristic kinetic properties were further examined to discriminate among different VOCs. The approach of using gas sensing kinetic analysis for multiple gas discrimination is an attractive solution to mitigate the problem of cross-sensitivity for resistive gas sensors.

Application of NMR Spectroscopy for the Detection of Equilibrating E-Z Diastereomers.

Conjugation can lower the energy barrier for unsaturated C-C bond rotations, resulting in a mixture of equilibrating diastereomers at room temperature. Therefore, methods claiming diastereoselective synthesis of conjugated double bonds often require proof that the observed diastereomeric ratio is not because of the diastereomeric equilibration of the product. Variable-temperature (VT) NMR experiments are commonly used to distinguish between the two possibilities. However, the VT technique requires accessories for the NMR spectrometer and more setup time. Here, we show that the rarely used application of 1-D and 2-D nuclear Overhauser effect spectroscopy experiments for the detection of the equilibrating diastereomers is a convenient alternative to the VT technique.

Parameter Estimation of Hemodynamic Cardiovascular Model for Synthesis of Photoplethysmogram Signal.

Synthesis of accurate, personalize photoplethysmogram (PPG) signal is important to interpret, analyze and predict cardiovascular disease progression. Generative models like Generative Adversarial Networks (GANs) can be used for signal synthesis, however, they are difficult to map to the underlying pathophysiological conditions. Hence, we propose a PPG synthesis strategy that has been designed using a cardiovascular system, modeled through the hemodynamic principle. The modeled architecture is composed of a two-chambered heart along with the systemic-pulmonic blood circulation and a baroreflex auto-regulation mechanism to control the arterial blood pressure. The comprehensive PPG signal is synthesized from the cardiac pressure-flow dynamics. In order to tune the modeled cardiac parameters with respect to a measured PPG data, a novel feature extraction strategy has been employed along with the particle swarm optimization heuristics. Our results demonstrate that the synthesized PPG is accurately followed the morphological changes of the ground truth (GT) signal with an RMSE of 0.003 occurring due to the Coronary Artery Disease (CAD) which is caused by an obstruction in the artery.

Detection of Counterfeit Medicines Using Hyperspectral Sensing.

Worldwide revenue of pharmaceutical market is more than 1200 billion USD [1] and that of counterfeit medicines is around 200 billion USD [2][3]. Counterfeit medicines can be detected by technical experts using visual inspection or through sophisticated lab and relevant methods. However, such methods require time, sample preparation and technical expertise with lab setup. These methods are not feasible and scalable to be used in the field by the general public. The objective of our research work was to detect counterfeit medicines using simpler and faster method using hyperspectral sensing. In this experiment, a visible - near infrared (350nm - 1050nm) hyperspectral device was used to capture spectral signature of the medicines. We used 24 medicine tablets of different companies. To imitate counterfeit medicines, tablet powders were adulterated by adding different levels of calcium carbonate. Spectral signatures were captured from original stage to all stages of adulterations and analyzed using machine learning (multilayer perceptron classifier). Result shows that we are able to achieve more than 90% classification accuracy. Portable hyperspectral sensing combined with medicines spectral database can be a good field level test method for detection of counterfeit medicines, as it is very fast, easy to use and does not require technical expertise.

Data-driven measurement of precision of components of pitch curves in Carnatic music.

Carnatic music (CM) is characterized by continuous pitch variations called gamakas, which are learned by example. Precision is measured on the points of zero-slope in gamaka- and non-gamaka-segments of the pitch curve as the standard deviation (SD) of the error in their pitch with respect to targets. Two previous techniques are considered to identify targets: the nearest semitone and the most likely mean of a semi-continuous Gaussian mixture model. These targets are employed irrespective of where the points of zero-slope occur in the pitch curve. The authors propose segmenting CM pitch curves into non-overlapping components called constant-pitch notes (CPNs) and stationary points (STAs), i.e., points where the pitch curve outside the CPNs changes direction. Targets are obtained statistically from the histograms of the mean pitch-values of CPNs, anchors (CPNs adjacent to STAs), and STAs. The upper and lower quartiles of SDs of errors in long CPNs (9-15 cents), short CPNs (20-26 cents), and STAs (41-54 cents) are separable, which justifies the component-wise treatment. The CPN-STA model also brings out a hitherto unreported structure in ragas and explains the precision obtained using the previous techniques.

MOMBAT: Heart rate monitoring from face video using pulse modeling and Bayesian tracking.

A non-invasive yet inexpensive method for heart rate (HR) monitoring is of great importance in many real-world applications including healthcare, psychology understanding, affective computing and biometrics. Face videos are currently utilized for such HR monitoring, but unfortunately this can lead to errors due to the noise introduced by facial expressions, out-of-plane movements, camera parameters (like focus change) and environmental factors. We alleviate these issues by proposing a novel face video based HR monitoring method MOMBAT, that is, MOnitoring using Modeling and BAyesian Tracking. We utilize out-of-plane face movements to define a novel quality estimation mechanism. Subsequently, we introduce a Fourier basis based modeling to reconstruct the cardiovascular pulse signal at the locations containing the poor quality, that is, the locations affected by out-of-plane face movements. Furthermore, we design a Bayesian decision theory based HR tracking mechanism to rectify the spurious HR estimates. Experimental results reveal that our proposed method, MOMBAT outperforms state-of-the-art HR monitoring methods and performs HR monitoring with an average absolute error of 1.329 beats per minute and the Pearson correlation between estimated and actual heart rate is 0.9746. Moreover, it demonstrates that HR monitoring is significantly improved by incorporating the pulse modeling and HR tracking.