Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving.

Increasing road crashes related to occupational drivers' deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers' conditions are important. However, in on-road driving, the relationship between drivers' physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers' physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35-63 years) were analyzed. Over a period of approximately three months, drivers' working behavior data, such as automotive sensor data, and their ANF data were collected during their working shift. Using the gradient boosting decision tree method, a rear-end collision risk index was developed based on the working behavior data, which enabled continuous risk quantification. Using the developed risk index and drivers' ANF data, effects of their physiological condition on risk were analyzed employing a logistic quantile regression method, which provides wider information on the effects of the explanatory variables, after hierarchical model selection. Our results revealed that in on-road driving, activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased each quantile of the rear-end collision risk index. The findings suggest that acute stress-induced drivers' fatigue increases rear-end collision risk. Hence, in on-road driving, drivers' physiological condition monitoring and ANF-based stress warning and relief system can contribute to promoting the prevention of rear-end truck collisions.

Proposal of layered mental healthcare for mental well-being.

A new concept, 'Layered mental healthcare' for keeping employees mental well-being in the workplace to avoid losses caused by both absenteeism and presenteeism is proposed. A key factor forming the basis of the concept is the biometric measurements over three layers, i.e., behaviour, physiology, and brain layers, for monitoring mental/distress conditions of employees. Here, the necessity of measurements in three layers was validated by the data-driven approach using the preliminary dataset measured in the office environment. Biometric measurements were supported by an activity tracker, a PC logger, and the optical topography; mental/distress conditions were quantified by the brief job stress questionnaire. The biometric features obtained 1 week before the measurement of mental/distress scores were selected for the best regression model. The feature importance of each layer was obtained in the learning process of the best model using the light graded boosting machine and was compared between layers. The ratio of feature importance of behaviour:physiology:brain layers was found to be 4:3:3. The study results suggest the contribution and necessity of the three-layer features in predicting mental/distress scores.

Mental Condition Monitoring Based on Multimodality Biometry.

We have developed a system with multimodality that monitors objective biomarkers for screening the mental distress in the office. A field study using a prototype of the system was performed over four months with 39 volunteers. We obtained PC operation patterns using a PC logger, sleeping time and activity levels using a wrist-band-type activity tracker, and brain activity and behavior data during a working memory task using optical topography. We also administered two standard questionnaires: the Brief Job Stress Questionnaire (BJS) and the Kessler 6 scale (K6). Supervised machine learning and cross validation were performed. The objective variables were mental scores obtained from the questionnaires and the explanatory variables were the biomarkers obtained from the modalities. Multiple linear regression models for mental scores were comprehensively searched and the optimum models were selected from 2,619,785 candidates. Each mental score estimated with each optimum model was well correlated with each mental score obtained with the questionnaire (correlation coefficient = 0.6-0.8) within a 24% of estimation error. Mental scores obtained by means of questionnaires have been in general use in mental health care for a while, so our multimodality system is potentially useful for mental healthcare due to the quantitative agreement on the mental scores estimated with biomarkers and the mental scores obtained with questionnaires.

Visualizing Worklog Based on Human Working Activity Recognition Using Unsupervised Activity Pattern Encoding.

Wearable motion sensor-based complex activity recognition during working hours has recently been studied to evaluate and thereby improve worker productivity. In the application of this technique to practical fields, one of the biggest challenges is performing time-consuming modeling tasks such as data labeling and hand-crafted feature extraction. One way to enable faster modeling is to decrease the time required for the manual tasks by making use of unlabeled motion datasets and the characteristics of complex activities. In this study, we propose a working activity recognition method that combines unsupervised encoding of the activity patterns of motions (denoted as "atomic activities"), the representation of working activities by combination of atomic activities, and the integration of additional information such as sensor time. We evaluated our method using an actual dataset from the caregiving field and found that it had an equivalent recognition performance (70.3% macro F-measure) to conventional hand-crafted feature extraction method. This is also comparable to that of previous methods using large labeled datasets. We also found that our method could visualize daily work processes with the accuracy of 71.2%. These results indicate that the proposed method has the potential to contribute to the rapid implementation of working activity recognition in actual working fields.

Relationship between truck driver fatigue and rear-end collision risk.

The fatigue of truck, bus, and taxi drivers has been a causal trigger for road accidents. However, the relationship between collision risk and the extent of objective fatigue has yet to be confirmed. In this study, we aimed to identify the relationship between autonomic nerve function as an objective parameter of fatigue and the extent of rear-end collision risk, which includes not only objectively risky events but also situations in which truck drivers require safety guidance from safety transport managers. Data of 33 truck driver participants (2 females, 31 males, 46.0 ± 9.1 years old, min-max: 24-65 years old) were analyzed. Drive recorder and automotive sensor data were collected over an eight-month period, and the autonomic nerve function during resting state in drivers was evaluated daily, pre- and post-shift, using pulse waves and electrocardiographic waveform measurement. The rear-end collision risk Index was developed using decision tree analysis of the audiovisual drive recorder data and distance data from the front automotive sensors. The rear-end collision risk index of shift-day was positively correlated with the sympathetic nerve activity index of post-shift condition on the previous day. This suggests that fatigue-related sympathetic nerve overactivity of post-shift condition increases the rear-end collision risk in the following day. Measures, such as actively seeking rest and undertaking fatigue recovery according to the degree of sympathetic nerve activity of post-shift condition, are necessary in order to prevent truck drivers' rear-end collisions.