<p>This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.</p> <p><br></p> <p>Overburdening an individual’s limited cognitive resources, especially when engaged in critical operations, may result in disastrous mishaps. Regular assessments of individuals’ physiological states and associated performance become vital to improving their mission readiness in such scenarios. As a key step towards a field-ready system, this treatise discusses the experimental findings pertinent to affective physiological state modulation and predictive modeling of marksmanship during a Go/NoGo shooting task in an immersive virtual reality environment. The shooting exercise requires the participants to hit the enemy targets and spare the friendly targets. The shooting difficulty levels (SDLs) are introduced by modulating the subject-specific target exposure time. The physiological signals used for analysis comprise electrocardiogram (ECG), 64-channel electroencephalogram (EEG), and standard shooting performance scores from 31 subjects. Experimental results with ECG features encompass involuntary physiologic process regulation and the interplay between the autonomic nervous system (ANS) components varying with SDL. Similarly, EEG features highlight the variations in brain region activations with SDLs. Predictive modeling of shooting performance (enemy hit, friendly spare, overall score) and behavioral response (mean enemy reaction time) from physiological (ECG and EEG) features evince the potency of physiological sensing for marksmanship estimation in operational contexts. Moreover, interpretable Shapley value analysis of the predictive models comprehend the (positive/negative) marginal impact of the underlying physiological features on marksmanship. This multimodal physiological sensing framework may assess the alterations in psychophysiological affective states and cognitive effects for performance analysis in operational contexts.</p>
This paper describes the design optimization and analysis of the digital hardware of a high-speed direct digital frequency synthesizer (DDFS) implemented using the NanGate 45nm Open Cell library. The digital blocks of the DDFS generate 13-bit accurate sinusoidal waveform in the frequency range of 0-500 MHz. The DDFS uses a 1.5 GHz input clock, a ROM-less phase to amplitude converter (PAC) based on the CORDIC algorithm, and has a frequency tuning resolution of 1 mHz. Fixed-point simulations and analysis were performed to obtain the finite hardware bit-widths to meet the desired Signal-to-Noise-Ratio (SNR) and Spurious-Free Dynamic Range (SFDR) performance. Multiple quantization schemes were compared and the optimum scheme, which meets the hardware timing constraints and the desired system performance, is selected for the final hardware implementation.
In the product development life-cycle of hard-real-time embedded systems, software verification plays a very important role. During verification, a Hardware-In-Loop (HIL) testbed is used to test the key properties of the software, namely, schedulability, concurrency and timeliness, by exercising and monitoring the interfaces of the Design Under Test (DUT). Often, building a testbed to test these properties can be challenging and costly. For the first time, this paper proposes a Modular Architecture with Event-driven Synchronization and Time-driven Real-time Operations (MAESTRO) for a low-cost testbed, to support HIL and system testing. Our proposed MAESTRO-based testbed is compared with other testbeds and is shown to provide superior performance. Finally, the paper also defines key properties essential for building a robust embedded testbed.
<p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. </p>
Coronary flow control mechanisms maintain the average coronary blood flow (CBF) at 4% of the cardiac output (CO) in normal adults, with no prior diagnosis of coronary artery disease (CAD), under resting conditions. This paper explores a pulsatile sixth order lumped parameter (LP) model of the cardiovascular system (CVS) which utilizes the average CBF approximated from CO along with arterial blood pressure (ABP) waveform to estimate the coronary microvascular resistance using non-linear least square optimization technique. The CVS model includes a third order model of the coronary vascular bed and is shown to achieve phasic coronary flow. The coronary epicardial resistance is varied to emulate different degrees of stenosis and achieve realistic behavior of coronary microvascular resistance under these conditions.
<p>This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. </p>
<p>"This work has been submitted to the IEEE Transactions on Affective Computing for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."</p><p><br></p><p>Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.</p>
Phonocardiogram (PCG) signal of the mitral valve prolapse (MVP) patients is characterized by transient audio events which include a systolic click (SC) followed by a murmur of varying intensity. Physicians detect these auscultation clues in regular auscultation before ordering expensive echocardio-graphy test. But auscultation is often error prone and even physicians with considerable experience might end up missing these clues. Therefore developing machine learning techniques to help clinicians is the need of the hour. A segmentation technique using Fourier synchrosqueezed transform (FSST) features with a long short term memory (LSTM) network is proposed in this study. An accuracy of 99.8% on MVP dataset demonstrates the potential of the proposed method in clinical diagnosis.
Physiological sensing of virtual reality (VR)-induced stressors are increasingly utilized to improve human training and assess the impact of gaming difficulty-induced stress on a person's health and well-being. However, the prior art sparsely explores the multi-level cardiovascular dynamics for psychophysiological demands in a VR environment. This treatise discusses the experimental findings and physiological interpretations of various heart rate variability (HRV) metrics extracted from 31 participants during a Go/No-Go VR-based shooting task across multiple timeframes. The VR-shooting exercise consists of firing at the enemy targets while sparing the friendly ones for different shooting difficulty levels: low-difficulty and high-difficulty with in-between baselines. Ex-perimental results demonstrate consistent shooting difficulty-induced stress patterns at multi-granular levels in response to the heterogeneous inputs (exogenous and endogenous factors). The physiological interpretations highlight the intricate inter-play between cardio-physiological components: sympathetic and parasympathetic response across multiple timescales (sessions and blocks) and shooting difficulty levels.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)
