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In recent years, when an older driver who cannot immediately recognize, judge, and operate properly faces an unexpected situation, they often panic, which may cause a traffic accident. However, there has not yet been enough discussion about the coping skills of older drivers in the face of this unexpected situation. Therefore, this study discusses the coping skills of older drivers in the face of unexpected situations. Moreover, we propose a coping skills prediction system (CP system). The CP system predicts coping skills from the tilt angle and angular velocity of the left foot when an older driver is driving or preparing to start a car. The experiment carried out two phases, a phase of driving a car and a phase of preparing to start the car. In the driving phase, the young and older driver drive the car in a driving simulator. The average age of the young driver group was ± standard deviation = 20.6 ± 0.7 years, and the age of the older driver group was 78.5 ± 5.1 years. The driving route included 15 cases in which collision accidents are likely to occur. We analyzed the experimental results of the driving phase and clarified the predictors of coping skills. Moreover, we analyzed the correlation between the left foot movement in driving and the left foot movement during preparing to start the car. As a result of the experiment, there was a 0.84 correlation between the tilt angle of the left foot of the older driver in driving and the tilt angle of the left foot of the older driver in preparing to start the car. The result shows that the coping skills can be predicted from the tilt angle of the left foot of the older driver during preparing to start the car. We showed that the coping skill can be predicted with an accuracy of 92% or 94% on average from the tilt angle and the angular velocity of the left foot while driving or preparing to start the car. Moreover, we clarified that the tilt angle of the left foot of a driver without coping skills is perpendicular to the ground compared to a driver with coping skills. This study is expected to contribute to the prevention of traffic accidents that occur in the face of an unexpected situation.

Many logistics companies adopt a manual order picking system. In related research, the effect of emotion and engagement on work efficiency and human errors was verified. However, related research has not established a method to predict emotion and engagement during work with high exercise intensity. Therefore, important variables for predicting the emotion and engagement during work with high exercise intensity are not clear. In this study, to clarify the mechanism of occurrence of emotion and engagement during order picking. Then, we clarify the explanatory variables which are important in predicting the emotion and engagement during work with high exercise intensity. We conducted verification experiments. We compared the accuracy of estimating human emotion and engagement by inputting pulse wave, eye movements, and movements to deep neural networks. We showed that emotion and engagement during order picking can be predicted from the behavior of the worker with an accuracy of error rate of 0.12 or less. Moreover, we have constructed a psychological model based on the questionnaire results and show that the work efficiency of workers is improved by giving them clear targets.

Multistory car parking system garages were introduced to increase the capacity of parking garages. However, the systems have problems, such as accidents (intrapark movement and theft), searching for parking spaces, awkward boarding/deboarding at narrow designated places, and difficulties in their use by beginners and elderly people. Several studies have been conducted to address these issues. One was a proposal to introduce automatic and electric pallets for transporting vehicles, and to realize automated valet parking for entering and exiting. An electric pallet was equipped with its own sensor, which made it easier to move and realize a more effective use of space in a parking lot. However, evaluations of the capacity of the parking lot and the time efficiency of exiting demonstrated that the method was insufficient. We propose herein a method of moving automatic pallets that improves the time required for exiting in automated valet parking. Specifically, we propose to reduce the exit distance (distance to the exit) by changing how automatic pallets are moved. In this study, various sensors were introduced to move automatic pallets accurately. In addition, considering the material as the detection target of the sensor, we selected the parking lot and the passage in the multistory car park as options. [ABSTRACT FROM AUTHOR]

When students feel depressed, their performance will decline, and they will not attend the university. To prevent non-attendance in the university, we propose a system of mood prediction using the majority vote based on a certainty factor (MVCF). When part of the data cannot be obtained, MVCF predicts the future mood from available data. Moreover, MVCF predicts four types of mood, namely, excitement, relaxedness, depression, and nervousness. Experimental results show that MVCF can predict moods from the next day until two weeks with 0.7 ± 0.1 accuracy. We clarified that weather and scheduled events contribute to predicting the future mood. MVCF predicts more types of mood than the existing system. Moreover, the accuracy of MVCF is equal to that of the existing system. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a sleep disorder detection system using pressure distribution sensors to examine and differentiate between a person with a sleep disorder and a healthy person. The Athens insomnia scale (AIS) questionnaire was used in this study. The participants were between 20 and 26 years old. A fabric-sheet pressure distribution sensor was used to collect data from the participants while they were sleeping. There is pressure from the body on the sensors while the participant is sleeping. Therefore, we can know the movement of the body from the measurement of this pressure. The sensor's sampling rate was 1/s. We observed different conditions of healthy and sleep disorder examinees while they were sleeping. The environment for the experiment was the participant's bedroom. Thus, they did not feel strange with this experiment. Data were analyzed using a support vector machine (SVM) to separate the data between healthy people and people with sleep disorders. [ABSTRACT FROM AUTHOR]

In contrast to traditional social network services (SNSs), event-based social networks determine close friendships (CFs) of users who share experiences and emotions with candidate friends in offline events. However, we could not provide feedback to cyberspace regarding the place, time, and target of a user realizing friendship since there is no technique for conveniently measuring the evocation of friendship during offline events. In this research, we propose a method of estimating the evocation of friendship using the similarity in the pulse rate variabilities (PRVs) of users when empathy is evoked between them. The user can be made aware of friendship estimated automatically through machine learning by wearing a wristwatch-type pulsimeter. CFs are more likely to evoke empathy than superficial friendships (SFs). To demonstrate the usefulness of this method, we conducted an experiment assuming an event where a group of four people are enjoying their time in an amusement park. From the experimental results, we showed that the similarity of the PRVs in CFs is greater than that in SFs when the favorability rating is high and the users like each other. Moreover, we showed that the proposed method estimated the evocation of friendship during the attraction experience with an f-measure of 0.74 at maximum and during an offline event with a mean f-measure of 0.78. The results showed the usefulness and effectiveness of this method. [ABSTRACT FROM AUTHOR]

Reactions of silanone(silyl)tungsten and -molybdenum complexes Cp*(OC)2M{O SiMes2(DMAP)}(SiMe3) (M = W (1a), Mo (1b), Cp* = η5-C5Me5, Mes = 2,4,6-Me3C6H2, DMAP = 4-(Me2N)C5H4N) with MesCNO, (Me2SiO)3, MeOH, and H2O were investigated. No silanone trapping product was detected in the reaction of 1 with MesCNO and (Me2SiO)3, while reactions of 1 with MeOH afforded the addition product Mes2Si(OMe)OH (5). The hydrolysis of molybdenum complex 1b afforded silanediol Mes2Si(OH)2 (8) as a main product with a trace amount of Mes2Si(OH)OSiMe3 (9), while that of the tungsten analogue 1a gave siloxysilanol 9 as the sole product, indicating that product distribution in the hydrolysis strikingly depends on the metal fragments. A theoretical investigation revealed that the product distribution difference between tungsten and molybdenum complexes arises from the difference in electrophilicity of the silicon atom in the silanone ligand and that of the lability for oxidative addition of water to the MII-L fragment. [ABSTRACT FROM AUTHOR]

The needs of older workers are increasing with the advancement of the aging society, and their health consciousness is also rising. Walking is a useful way of maintaining health and wellness in the elderly. Walking is also carried out in tours at tourist sites. It leads to health maintenance and the improvement of the willingness to work as well as satisfying the thirst for knowledge at a visited site. The limit of number of steps should be understood accurately at that time. The number is derived from data measured over one year in this study. The subject was a 65 year-old male. The limit in the number of steps was about 12000 based on this experiment. He plays tennis one or twice a week and is in relatively good health. A walking experiment was carried out in a good viewing location, and data such as vital signs were collected. The experiment was carried out within the limit of step number. It was found that there was a strong correlation between the number of steps and the amount of physical exertion. The mood grade of the subject decreased after over an hour, and he became a bit tired of walking. It is suggested that walking for about 90 min is appropriate for a cheerful older person. [ABSTRACT FROM AUTHOR]

Depression has become a social problem in Japan. To prevent depression, people need to recognize their mental health in daily life. Previous research supports mental health care by predicting tomorrow's mood with 73% accuracy using weather information and biological information. However, the mood after 2 d or later could not be predicted using an existing system. In this paper, we propose multilayer-deep neural network (M-DNN) and storage-type prediction models (STPMs) to predict mood two weeks in advance with high accuracy. The M-DNN outputs predictions as well as unpredictable data using a deep neural network and threshold optimization in each prediction layer. The threshold optimization determines the threshold that maximizes a certainty factor. The certainty factor is calculated from the predictive accuracy of M-DNN and the amount of unpredictable data. The STPMs interpolates the unpredictable data by accumulating the predictions output from M-DNN. The amount of unpredictable data output from the M-DNN is decreased by STPMs. Experiments show that M-DNN and STPMs can predict mood two weeks in advance with 70% accuracy. The predictive accuracy in M-DNN+STPMs is 11% higher than that in DNN. Hence, M-DNN+STPMs is an effective method for mood prediction. [ABSTRACT FROM AUTHOR]

Because of Japan's rapidly aging population, the number of single-person households is increasing. Therefore, to ensure the safety of residents, the demand for methods of monitoring residents' behaviors and indoor situations has become higher. Instead of using devices that can violate a person's privacy, such as cameras or microphones, we focus on plant bioelectric potential for monitoring. The plant is not harmful to the resident; moreover, it may have healing effects on humans. Furthermore, previous studies show that the potential is affected by human behavior around it. Our study applies this property, and we propose a method of estimating the location of a resident in an indoor environment. In this work, we build a classification and regression model to determine the distance between plants and humans. Then, we place two or more plants in one room and estimate the distance from the resident to each plant using the built models. Then, on the basis of these estimation results, the location of the resident is estimated. Furthermore, we conduct an evaluation to compare the estimated result and the resident's location history and show the accuracy of our method. [ABSTRACT FROM AUTHOR]

Visually impaired people are limited in movement both indoors and outdoors. To solve this problem, existing studies propose a navigation system that leads visually impaired people on the route to their destination by detecting obstacles on the route and tracking their position using information and communication technology. Visually impaired people recognize their position and the route to their destination by combining these positioning methods. On other hand, existing studies propose detection of obstacles by image processing, but this method take a long time. Hence, we have proposed the high-speed detection of a down staircase for visually impaired people. The proposed method quickly detects the boundary line between the down staircase and floors using the illuminance of a point pattern. Moreover, the proposed method recognizes the down staircase quickly because it removes images that are clearly different from the down staircase and applies image processing to the pedestrian route only. Experimental results have shown that the proposed method can recognize a down staircase and border line with more than 90% accuracy. As a result, visually impaired people can safely go down a flight of stairs using this method. [ABSTRACT FROM AUTHOR]

In this study, we aim to develop and improve an attribute classification method for pedestrians using a plantar pressure value. At present, many retail businesses use various methods (e.g., member's card, direct mail, questionnaire, manual classification by staff members) of collecting customer information. However, these methods have some problems. One of the problems is the instability of collecting customer information. The member's card cannot cover all customers. Moreover, manual classification includes dispersion by individual difference. However, using pressure sensors has advantages. One of the advantages is that the pressure sensor does not violate the targeted person's privacy, because the pressure values from the sensors cannot identify one individual from a large indefinite number. Another advantage is that the pressure sensor can be measured without being affected by the environment (e.g., lighting, field of view, focus range). Our method of attribute classification uses four feature quantities, which are the CoG (center of gravity in the X-axis and the Y-axis), plantar area, and total pressure value. In the experimental results, the classification accuracy for gender was 72.19% and that for three age groups was 42.33%. [ABSTRACT FROM AUTHOR]

Base-stabilized silanone molybdenum complexes were synthesized by the oxygenation of the MvSi bond in the silyl(silylene)molybdenum complex with 1 eq. of PNO in the presence of Lewis base L. The PNOcoordinated silanone complex (L = PNO) was converted to cis-[Cp*-(OC)2.Mo{OSiMe3(OSiMeS2)}(PMe3)] in the presence of excess PMe3. [ABSTRACT FROM AUTHOR]