Evaluation of Correlation between Psychomotor Vigilance Task Scores and Drowsiness Estimation Levels Obtained from Facial Videos.

This paper reports the results of an experiment to evaluate the relationship between results obtained with a drowsiness estimation system we have developed using facial videos and those obtained with the Psychomotor Vigilance Task (PVT), which is a standard index of sleepiness used in sleep medicine. The correlation between PVT scores and the output of the drowsiness estimation system, which outputs drowsiness levels from assigned facial expressions, was calculated using data from 30 subjects. The Spearman's correlation coefficients between the drowsiness estimation results and the PVT mean response time, the slowest 10% response time, and the number of lapses were 0.36 (p <0.001), 0.43 (p <0.001), and 0.40 (p <0.001), respectively. Since this experiment showed a correlation between the drowsiness estimation results and those with PVT, it would seem possible to make specific interventions based on drowsiness estimation results learned from ground-truth drowsiness levels. Such estimation results could help prevent accidents resulting from drowsiness or insufficient vigilance while driving or working.

Development of a Real-time Chronic Stress Visualization System from Long-term Physiological Data.

We have developed a real-time system which can estimate and display chronic stress levels determined from a long-term physiological data. It consists of wearable sensors that measure physiological data, a smartphone application that receives data from the sensors and displays chronic stress levels, and a cloud system that estimates them on the basis of received data. To operate it, we have to treat irregularly uploaded user-physiological-data of varying sizes, calculate chronic stress levels from long-term features without delay on a daily basis, and display them in real-time on the smartphone application. For this purpose, we have developed a system that requires relatively little memory and processing time with one six-hundredth of maximum memory usage and one twentieth of processing time as compared to conventional method by subdividing uploaded physiological data, calculating features from them, and creating long-term features by combining the subdivided features.

Accurate Chronic Stress Estimation with Personalized Models Based on Correlation Maximization.

We propose an accurate chronic stress estimation system that utilizes personalized models based on correlation maximization between physiological features and ground truth, which helps determine physiological features effective for the estimation. The personalized models are trained using features respectively found for each individual classes among which the relationships between features and ground truth differ. Which class a new user belongs to can be estimated from the results of a personality questionnaire, as well as by means of conventional methods. W.r.t. evaluation data, with the cooperation of 168 subjects, 599 sets of 1-month wearable-sensor data and ground-truth Perceived Stress Scale (PSS) data were collected, along with the Big Five Personality Traits for each subject. In chronic stress estimation evaluations using this above data, we have confirmed that the proposed classification system achieved 69.1% estimation accuracy in terms of increase/decrease in PSS, as compared to 59.3% and 56.8% achieved, respectively, with two conventional methods, one employing no classification and the other employing k -means clustering.

Interactive Effects of Productivity and Work Engagement on Mediation Analysis using Chronic Stress as Explanatory variable.

Productivity, stress, and work engagement play important roles in corporate health. In this study, we have investigated, on the basis of survey data, interactive-effect relationships among productivity, stress, and work engagement. Survey results obtained from 301 samples self-report questionnaires (including the WHO Health and Work Performance Questionnaire: HPQ; the Perceived Stress Scale: PSS; and the Utrecht Work Engagement Scale: UWES) were analyzed using mediation analysis. Results suggest that the interactive positive effects of productivity and work engagement were roughly equal, and that stress decreased both productivity and work engagement. Revealing the relationships among productivity, stress, and work engagement contributes to the efforts of occupational health physicians and of workers in human resource departments trying to plan effective and preferential interventions in order to improve employee working conditions.

Forecasting stress, mood, and health from daytime physiology in office workers and students.

We examine the problem of forecasting tomorrow morning's three self-reported levels (on scales from 0 to 100) of stressed-calm, sad-happy, and sick-healthy based on physiological data (skin conductance, skin temperature, and acceleration) from a sensor worn on the wrist from 10am-5pm today. We train automated forecasting regression algorithms using Random Forests and compare their performance over two sets of data: "workers" consisting of 490 days of weekday data from 39 employees at a high-tech company in Japan and "students" consisting of 3,841 days of weekday data from 201 New England USA college students. Mean absolute errors on held-out test data achieved 10.8, 13.5, and 14.4 for the estimated levels of mood, stress, and health respectively of office workers, and 17.8, 20.3, and 20.4 for the mood, stress, and health respectively of students. Overall the two groups reported comparable stress and mood scores, while employees reported slightly poorer health, and engaged in significantly lower levels of physical activity as measured by accelerometers. We further examine differences in population features and how systems trained on each population performed when tested on the other.

Model Predictive Control of Shallow Drowsiness: Improving Productivity of Office Workers.

This paper proposes a methodology of model predictive control for alleviating shallow drowsiness of office workers and thus improving their productivity. The methodology is based on dynamically scheduling setting values for air conditioning and lighting to minimize the drowsiness level of office workers on the basis of a prediction model that represents the relation between the future drowsiness level and a combination of indoor temperature and ambient illuminance. The prediction model can be identified by utilizing a state-of-the-art drowsiness estimation method. The proposed methodology was evaluated in a real routine task (performed by six subjects over five workdays), and the evaluation results demonstrate that the proposed methodology improved the workers' processing speed by 8.3% without degrading their comfort.

Sleep/wake classification via remote PPG signals.

This paper proposes a remote sleep/wake classification method by combining vision-based heart rate (HR) estimation and convolutional neural network (CNN). Instead of inputting the estimated HR with low temporal resolution, remote PPG (Photoplethysmogram) signals, which contain high-temporal-resolution HR information, are input into the CNN. To reduce noise in the remote PPG signals, we propose a dynamic HR filter. Evaluation results show that the dynamic HR filter works more effectively in comparison with the static filter, which helps improve the area under the ROC curve (AUC) to 0.70, which is almost as good as the reference 0.71 for HR from a wearable sensor.

A neural-network-based investigation of eye-related movements for accurate drowsiness estimation.

Many studies reported that eye-related movements, e.g., blank stares, blinking and drooping eyelids, are highly indicative symptoms of drowsiness. However, few researchers have investigated the computational efficacy accounted for drowsiness estimation by these eye-related movements. This paper thus analyzes two typical eye-related movements, i.e., eyelid movements Xel(t) and eyeball movements Xeb(t), and investigates neural-network-based approaches to model temporal correlations. Specifically, we compare the effectiveness of three combinations of eye-related movements, i.e., [Xel(t)], [Xeb(t)], and [Xel(t),Xeb(t)], for drowsiness estimation. Furthermore, we investigate the usefulness of two typical types of neural networks, i.e., CNN-Net and CNNLSTM-Net, for better drowsiness modeling. The experimental results show that [Xel(t),Xeb(t)] can achieve a better performance than [Xel(t)] for short time drowsiness estimation while [Xeb(t)]alone performs worse even than the baseline method (PERCLOS). In addition, we found that CNN-Net are more effective for accurate drowsiness level modeling than CNNLSTM-Net.

Head-motion Robust Video-based Heart Rate Estimation Using Facial Feature Point Fluctuations.

Interest in measuring heart rates (HRs) without physical contact has increased in the area of stress checking and health care. In this paper, we propose head-motion robust video-based heart rate estimation using facial feature point fluctuations. The proposed method adaptively estimates and removes such rigid-noise components as noise stemming from horizontal head motion and extracts relatively small heart signals. Rigid-noise components can be accurately estimated and removed by using changes in facial feature points which are not dominant over heart signals and are more dominant over noise signals than are such luminance signals as RGB. In evaluation experiments on a benchmark dataset, our method achieved the highest accuracy among state-of-the-art methods.