Recently, the interest in biometric authentication based on electrocardiograms (ECGs) has increased. Nevertheless, the ECG signal of a person may vary according to factors such as the emotional or physical state, thus hindering authentication. We propose an adaptive ECG-based authentication method that performs incremental learning to identify ECG signals from a subject under a variety of measurement conditions. An incremental support vector machine (SVM) is adopted for authentication implementing incremental learning. We collected ECG signals from 11 subjects during 10 min over six days and used the data from days 1 to 5 for incremental learning, and those from day 6 for testing. The authentication results show that the proposed system consistently reduces the false acceptance rate from 6.49% to 4.39% and increases the true acceptance rate from 61.32% to 87.61% per single ECG wave after incremental learning using data from the five days. In addition, the authentication results tested using data obtained a day after the latest training show the false acceptance rate being within reliable range (3.5–5.33%) and improvement of the true acceptance rate (70.05–87.61%) over five days.

Given that light is known to function as a zeitgeber, having the greatest influence on the human circadian rhythm, it is necessary to assess the effects of light on humans with the goal of maintaining the circadian rhythm. Herein, we fabricated a simple circadian light meter that directly measures the non-visual effects of light using optical filters that mimic the non-visual action spectrum. The fabricated light meter was calibrated and verified through the values obtained from a conventional illuminance spectrophotometer. Furthermore, during 24 h of everyday life, 11 participants wore hats equipped with the developed light meter so that we could investigate the effects of the light environment to which they were exposed to, both indoors and outdoors. For comparison, natural outdoor illumination was also measured with the same light meter. Based on the considerable difference between the light exposure levels during the daytime and nighttime, it is possible that the participant’s melatonin levels would be impacted by the light exposure measured by the light meter. Consequently, based on the light exposure measurements made in this study, the proposed circadian light meter would be a valuable tool for real world circadian lighting studies that require actual light dose to the eyes of the test subjects.

Understanding the non-visual effects of light is increasingly important, as circadian disruption from inappropriate exposure negatively impacts sleep, metabolism, and cognition. However, artificial lighting is rarely designed from a non-visual illuminance perspective, and its effects on biological rhythms remain incompletely evaluated. Here, we investigated three lighting conditions in rats: conventional light–dark (L/D), and two novel settings with matched visual illuminance but differing nighttime rat melanopic equivalent daylight illuminance (2–4 lx; rat mel EDI), enabled by tunable four-package white light-emitting diodes. Circadian rhythms were assessed using physiological (core body temperature, heart rate, locomotor activity) and neurophysiological markers (non-rapid eye movement sleep index, θ–γ cross-frequency coupling) across rhythmicity (periodicity, amplitude), stability (inter-daily stability, intra-daily variability), and state classification (Gaussian mixture modeling, k-means clustering). Relative to 4 lx, the 2 lx lighting most closely resembled L/D in minimizing rhythm loss and maintaining sleep–wake synchrony. Furthermore, after circadian disruption, switching to the 2 lx lighting restored physiological and neurophysiological markers. These findings indicate that spectrally optimized 2 lx lighting is a viable strategy to maintain or reinstate circadian health when nighttime illumination is unavoidable.