Emi Yuda 교수

With the popularization of pulse wave signals by the spread of wearable watch devices incorporating photoplethysmography (PPG) sensors, many studies are reporting the accuracy of pulse rate variability (PRV) as a surrogate of heart rate variability (HRV). However, the authors are concerned about their research paradigm based on the assumption that PRV is a biomarker that reflects the same biological properties as HRV. Because PPG pulse wave and ECG R wave both reflect the periodic beating of the

Vagal cardiac modulation is suppressed by OLED blue light in healthy subjects most likely through melanopsin-dependent non-image-forming effect.

The effects of blue OLED light for maintaining autonomic and psychomotor arousal levels depend on both absolute and relative contents of melanopic component in the light.

Exposure to blue light during lunch break, compared with that to orange light, enhances autonomic arousal during exposure, but has no sustained effect on autonomic arousal or behavioral alertness after exposure.

Although heart rate variability and actigraphic data have been used for sleep-wake or sleep stage classifications, there are few studies on the combined use of them. Recent wearable sensors, however, equip both pulse wave and actigraphic sensors. This paper presents results on the performance of sleep stage classification by a combination of heart rate and actigraphic signals. We studied 40,643 epochs (length 3 min) of polysomnographic data in 289 subjects. A combined model, consisting of autono

These suggest that PRV shows not only systemic differences from HRV but also considerable inter-site variations.

High-risk predictors of HRV and heart rate dynamics tend to cluster in the same person, indicating a high degree of redundancy between them.

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a

Nocturnal heart rate variability (HRV) is thought to reflect healthy recovery function of the autonomic nervous system. Although exercise is recommended for health promotion, exercise itself decreases HRV. We studied acute effect of daytime exercise on nocturnal HRV in 5 healthy adults (age, 22-40 years; 2 female subjects) without regular exercise habit. Using a treadmill, they performed 30-min walking at 4 km/hr and 30-min running at 9 km/hr from 11 a.m. on different days at an interval of 2 we

Hesitations and extended error correction time can be associated with increased crash risk due to unexpected runaway by older drivers. The system we have developed may help to uncover and evaluate physiological characteristics related to crash risk in the elderly population.

To examine the basic characteristics of pulse rate variability (PRV) during work, pulse wave signals during work hours were recorded by wristband-type wearable sensors in 8 office workers for 1-3 months together with the signals of physical activity, skin temperature, and the amount of conversation and with subjective emotions. A total of 1,544 hours of data were obtained. Pulse rate increased during physical activity and decreased during dozing. Although high frequency (HF) component of heart r

Abstract Objective Blue light has been attributed to the adverse biological effects caused by the use of smartphones and tablet devices at night. However, it is not realistic to immediately avoid nighttime exposure to blue light in the lifestyle of modern society, so other effective methods should be investigated. Earlier studies reported that inferior retinal light exposure causes greater melatonin suppression than superior retinal exposure. We examined whether the autonomic responses to blue l

Many studies have reported method to detect car driver drowsiness by heartbeat interval signals, but it is hard to record stable signals while driving in a way that does not burden the drivers. We examined whether the driver’s drowsiness can be detected from respiration signal. In 9 healthy subjects (seven males and two females; age, 45 ± 9 y), respiration, electrocardiogram, and acceleration signals were recoded for a total of 2,359 min (137-468 min per subject) of driving with a smart shirt bi