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.