A nociceptor is a critical and special receptor of a sensory neuron that is able to detect noxious stimulus and provide a rapid warning to the central nervous system to start the motor response in the human body and humanoid robotics. It differs from other common sensory receptors with its key features and functions, including the “no adaptation” and “sensitization” phenomena. In this study, we propose and experimentally demonstrate an artificial nociceptor based on a diffusive memristor with critical dynamics for the first time. Using this artificial nociceptor, we further built an artificial sensory alarm system to experimentally demonstrate the feasibility and simplicity of integrating such novel artificial nociceptor devices in artificial intelligence systems, such as humanoid robots.

PURPOSE. Retinal vein pulsation properties are altered by glaucoma, intracranial pressure (ICP) changes, and retinal venous occlusion, but measurements are limited to threshold measures or manual observation from video frames. We developed an objective retinal vessel pulsation measurement technique, assessed its repeatability, and used it to determine the phase relations between retinal arteries and veins.METHODS. Twenty-three eyes of 20 glaucoma patients had video photograph recordings from their optic nerve and peripapillary retina. A modified photoplethysmographic system using video recordings taken through an ophthalmodynamometer and timed to the cardiac cycle was used. Aligned video frames of vessel segments were analyzed for blood column light absorbance, and waveform analysis was applied. Coefficient of variation (COV) was calculated from data series using recordings taken within 61 unit ophthalmodynamometric force of each other. The time in cardiac cycles and seconds of the peak (dilation) and trough (constriction) points of the retinal arterial and vein pulse waveforms were measured.RESULTS. Mean vein peak time COV was 3.4%, and arterial peak time COV was 4.4%. Lower vein peak occurred at 0.044 cardiac cycles (0.040 seconds) after the arterial peak (P ¼ 0.0001), with upper vein peak an insignificant 0.019 cardiac cycles later. No difference in COV for any parameter was found between upper or lower hemiveins. Mean vein amplitude COV was 12.6%, and mean downslope COV was 17.7%. CONCLUSIONS.This technique demonstrates a small retinal venous phase lag behind arterial pulse. It is objective and applicable to any eye with clear ocular media and has moderate to high reproducibility. (http://www.anzctr.org.au number, ACTRN12608000274370.)

Respiratory ailments afflict a wide range of people and manifests itself through conditions like asthma and sleep apnea. Continuous monitoring of chronic respiratory ailments is seldom used outside the intensive care ward due to the large size and cost of the monitoring system. While Electrocardiogram (ECG) based respiration extraction is a validated approach, its adoption is limited by access to a suitable continuous ECG monitor. Recently, due to the widespread adoption of wearable smartwatches with in-built Photoplethysmogram (PPG) sensor, it is being considered as a viable candidate for continuous and unobtrusive respiration monitoring. Research in this domain, however, has been predominantly focussed on estimating respiration rate from PPG. In this work, a novel end-to-end deep learning network called RespNet is proposed to perform the task of extracting the respiration signal from a given input PPG as opposed to extracting respiration rate. The proposed network was trained and tested on two different datasets utilizing different modalities of reference respiration signal recordings. Also, the similarity and performance of the proposed network against two conventional signal processing approaches for extracting respiration signal were studied. The proposed method was tested on two independent datasets with a Mean Squared Error of 0.262 and 0.145. The cross-correlation coefficient of the respective datasets were found to be 0.933 and 0.931. The reported errors and similarity was found to be better than conventional approaches. The proposed approach would aid clinicians to provide comprehensive evaluation of sleeprelated respiratory conditions and chronic respiratory ailments while being comfortable and inexpensive for the patient.

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This is a literature review paper covering state-of-the-art sleep technologies to measure sleep and clinical sleep disorders. This paper addresses an interdisciplinary audience from a variety of subdomains in engineering and medicine. We reviewed 120 scientific papers, 15 commercial mobile apps, and 4 commercial devices. We selected the papers from scientific publishers including Institute of Electrical and Electronics Engineers (IEEE), Nature, Association for Computing Machinery (ACM), Proceedings of Machine Learning Research, Journal of Informatics in Health and Biomedicine, Plos One, PubMed, and Elsevier and Nature digital libraries. We used Google Scholar with keywords including "sleep monitoring", "sleep monitoring technologies", "non-contact sleep monitoring", "mobile apps for sleep monitoring", "AI in sleep technologies", and "automated sleep staging." The manuscript reviews sleep technologies, including sleep lab technologies such as polysomnography and consumer sleep technologies categorized as ambient room sensors, wearable sensors, bed sensors, mobile apps, and artificial intelligence. We primarily focused on validation and comparison studies of the reviewed technologies. The manuscript also provides an overview of several clinical datasets for sleep staging and taxonomizes the different learning methods. Finally, the manuscript offers our insights and recommendations about the application of the reviewed sleep technologies.

The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true "thinking machine". Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs, relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor (CMOS) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOS networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess.

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Continuous monitoring of cardiac health under free living condition is crucial to provide effective care for patients undergoing post operative recovery and individuals with high cardiac risk like the elderly. Capacitive Electrocardiogram (cECG) is one such technology which allows comfortable and long term monitoring through its ability to measure biopotential in conditions without having skin contact. cECG monitoring can be done using many household objects like chairs, beds and even car seats allowing for seamless monitoring of individuals. This method is unfortunately highly susceptible to motion artifacts which greatly limits its usage in clinical practice. The current use of cECG systems has been limited to performing rhythmic analysis. In this paper we propose a novel end-to-end deep learning architecture to perform the task of denoising capacitive ECG. The proposed network is trained using motion corrupted three channel cECG and a reference LEAD I ECG collected on individuals while driving a car. Further, we also propose a novel joint loss function to apply loss on both signal and frequency domain. We conduct extensive rhythmic analysis on the model predictions and the ground truth. We further evaluate the signal denoising using Mean Square Error (MSE) and Cross Correlation between model predictions and ground truth. We report MSE of 0.167 and Cross Correlation of 0.476. The reported results highlight the feasibility of performing morphological analysis using the filtered cECG. The proposed approach can allow for continuous and comprehensive monitoring of the individuals in free living conditions.