The history of medicine shows that myocardial infarction is one of the significant causes of death in humans. The rapid evolution in autonomous technologies, the rise of computer vision, and edge computing offers intriguing possibilities in healthcare monitoring systems. The major motivation of the work is to improve the survival rate during a cardiac arrest through an automatic emergency recognition system under ambient intelligence. We present a novel approach to chest pain and fall posture-based vital sign detection using an intelligence surveillance camera to address the emergency during myocardial infarction. A real-time embedded solution persuaded from “edge AI” is implemented using the state-of-the-art convolution neural networks: single shot detector Inception V2, single shot detector MobileNet V2, and Internet of Things embedded GPU platform NVIDIA’s Jetson Nano. The deep learning algorithm is implemented for 3000 indoor color image datasets: Nanyang Technological University Red Blue Green and Depth, NTU RGB + D dataset, and private RMS dataset. The research mainly pivots on two key factors in creating and training a CNN model to detect the vital signs and evaluate its performance metrics. We propose a model, which is cost-effective and consumes low power for onboard detection of vital signs of myocardial infarction and evaluated the metrics to achieve a mean average precision of 76.4% and an average recall of 80%.
This paper is an attempt to realize a unique feature of visual brain on CMOS circuit. There is a binocular rivalry phenomenon of visual brain in which, if two different images are shown to left and right eye then they compete for perceptual dominance such that one image become visible while the other get suppressed. Thus inspired from the concepts of neuromorphic and bio-inspired computing circuits, we would like to explain various features of binocular rivalry (BR) using Adaptive Winner Takes All (WTA) CMOS circuit. The application of this work lies in the fact that CMOS circuit can be implanted in the robot brain and hence opens a window in the area of artificial intelligence. The circuit is simulated using BSIM3 level 49 MOSFET models using T-Spice 0.35μm CMOS process. The key structure is an electronic synapse which is based around a floating-gate pFET. Thus the simulated results of the circuit depict two main features of binocular rivalry i.e., dominance and suppression of images and neural adaption. The circuit consumes 0.4mW total power, shows thermal stability of about 1.23μA/°C and occupies 25μm×20 μm chip area. Moreover a third and remarkable feature in BR is focused attention, which may considerably influence the binocular rivalry switching and can be attained using meditation. We would like to modify the circuit with additional, control of adaption at floating gate, circuitry to realize the later feature as well. The paper catered the later feature in future work.
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