The use of VR technology is growing in a number of fields, but there are still some problems that need solving. One of said problems is the improvement of interaction within the VR space and how user interfaces are displayed. In this study, research has been conducted on how users feel using a simple, solid figure. In order to investigate better user interfaces in VR space and more effective ways of displaying information. Tasks were conducted to recognize the trajectory of an object’s movements in virtual space using VR contents. This was used and an investigation was conducted into how much recognition was affected by the speed of an object’s movement, the range of the object’s motion, and how far an object is away. As a result, it was found that the appropriate distance needed to recognize a dynamic body’s movement, proved that there is a tendency for the rate of accurate recognition to rise the wider the range of motion is, and learned that recognition is not affected if the speed of movement is within the range where ocular following responses are possible. In addition, it was proved that a combination of distance and speed heavily influences recognition of a dynamic body.

Research on attractiveness prediction using facial features has been actively conducted in recent years. The use of convolutional neural networks (CNNs) has been reported to achieve highly accurate predictions. In this study, we investigated facial attractiveness factors by visualizing the hidden layer of the constructed CNN model and enabled the confirmation of features that are important for prediction. The features extracted from this model were moderately consistent with the findings of psychological research.

A rapid evacuation is important for reducing damage caused by major earthquakes. This research focuses on an emergency alert system applied during an earthquake, specifically, a system that provides an emergency alert interval between two mono sounds. Adequate research has yet to be conducted in this field; therefore, intuition is important. The present research is useful for analyzing ways to reduce the damage caused by large disasters. A total of 36 students participated. To search for an emergency alert interval between two mono sounds, we used an anklet to search for an image. We then revived the association using a factor analysis. As a result, we divided the 10 evaluation factors into three groups and labeled them the unrest factor, impact factor, and hardness factor. The unrest factor was connected to the alert chord, and the impact factor was connected to the sound interval of the alert. Thus, emergency alerts evaluated these two factors.

We evaluated physical function, physical activity, and sleep status in five elderly individuals with musculoskeletal ambulatory disability symptom complex (MADS) for one year, and examined their influential factors associated with sarcopenia. Compared with the baseline, the results did not show significant differences in every item. Notably, the COVID-19 pandemic had occurred throughout Japan for nearly the entire study period. The impact on psychological parameters of participants was surveyed using the Profile of Mood States second edition (POMS 2R); the results suggested that their mood states were relatively stable. Although we can consider these results, i.e., the participants maintained a high level of health consciousness and willingness to participate in social activities and physical exercise even under various restrictions, we were able to trace an extremely small number of participants because of the pandemic. This study shall be continuously conducted to record measurable data every 3 months, and aim for the development of device-free systems that prevent fractures in elderly individuals with MADS.

We have been developing an electroencephalography (EEG)-based brain-machine/computer interface (BMI/BCI) that uses event-related potentials (ERPs) as a "mind switch." ERP reflects a temporal change in attention and is also known as a potential biomarker for degrading cognitive functions, which can be applied to older people even with motor decline. In this study, we focused on this characteristic of ERP to develop a novel cognitive assessment system, "Neurodetector." This system was designed to evaluate a subject's cognitive function according to his/her success rate of a cognitive task performed by the mind switch. As the first step to establishing a proof of concept, we recorded EEG data from 40 healthy adult subjects (under 65 years old) during 3 cognitive tasks with varying difficulty levels. As a result, subjects could successfully control the mind switch (elicit detectable ERP) to perform all tasks beyond the chance level, although success rates varied among individuals. Furthermore, the average success rate for the 3 tasks gradually increased as the task became easier. These results suggest that the success rate is an efficient single-index that reflects the degree of cognitive load within a subject. Following this research, we will be studying the cause of differences between subjects to further explore this index's effectiveness as a cognitive biomarker for clinical assessment. As a future prospectus, we vision the Neurodetector to be applied for early detection of dementia.

The purpose of this study is to evaluate physiological and psychological stress in truck driver towing self-driving trucks. The truck A with which two self-driving truck B and C are coupled in series is run along a guide line expressway for running for about 30 minutes, 3times in a day. This experiment last 3 days, with 3 subjects derived the lead truck while towing 2 self-driving truck. For safety, driver was on the self-driving trucks B and C. In addition, each subject towing self-driving operation 3 times, solo operation (not towing self-driving) was 1 time. The bio-signal data (ECG, body temperature, skin temperature) of the subject were continuously measured at each driving phase. Subject's data phase "solo" compares with the towing data compared to detect the difference. As a result of those data, we have significant increase in skin temperature and heart rate between solo and towing operations.

Today's entrepreneurship education in Japan is mainly based on knowledge- and skill-oriented programs in the style of Silicon Valley in the United States. However, for university students belonging to Generation Z, the motivation for entrepreneurship tends to be not only wealth, but also altruistic "meaning" such as social contribution and connection with others. Self-awareness of the purpose and implications of entrepreneurship is necessary, but there are very few opportunities for university students to become self-aware in entrepreneurship education. However, Nonaka's SECI model and Weick's concept of Sensemaking are beginning to attract attention, and specific methodologies for self-awareness are desired. The author proposed a methodology to realize self-awareness from the viewpoint of Affective Engineering in a paper published in ISASE2020 on "knowing oneself. In addition, in the paper published in IJAE 2020, I verified the effectiveness of affective sense of value extraction. In this paper, the author discuss the importance of the process of Sensemaking, which has been difficult to supplement in business administration and entrepreneurship research, from the viewpoint of Affective engineering, and propose a rubric as self-assessment tool for PBL participants.

Consumers who value superficial performance and design are more likely to change their minds when a brand with higher performance or a more fashionable design emerges. On the other hand, loyal consumers understand, sympathize with, and value brand concept. Therefore, their repurchase rate is high and they are willing to pay a higher amount than for similar products from other companies. However, in marketing practice and research, there is little awareness of evaluating whether or not the brand concept is being communicated to consumers. Therefore, in this study, the relationship between concept recognition and willingness to pay was clarified. Consequently, the study confirmed that there is a significant difference in the willingness to pay in consumers who recognize the brand concept. This result provides a suggestion to reaffirm the significance of the concept so as not to waste excellent means and technologies.

This paper describes our approach to the design and implementation of virtual Kawaii robots and spaces by Japanese and American university students using remote collaboration. Because of the COVID-19 pandemic, we had to change our planned 7-week collaboration from in-person to virtual with a resultant change in the target product of our collaboration from real robots to virtual robots. Based on our new plan, students designed virtual spaces with robot pairs, proposed evaluation items for the robot pairs, evaluated their designs, and analyzed the results. The students designed each robot pair with the goal that one robot would be more kawaii and the other would be less kawaii due to a variation in a single attribute such as shape or color. The evaluation instrument used adjective pairs that were suitable to evaluate the affective values of the robot pairs and the virtual spaces the robots occupied. Through the design experience, students learned a lot about Kawaii Engineering and affective evaluation, which gave them a deeper understanding of Japanese culture from the viewpoint of Kansei/Affective Engineering.

We investigated the interactive effect of garment style and textile patterns on garment impression. We used eight textile patterns and three garment styles to generate twenty-seven simulated garment images: three images of unpatterned garments and twenty-four images of patterned garments. We then conducted a sensory evaluation of these stimuli using the semantic differential method. Eight images of textile patterns were also used in the sensory evaluation. A two-way ANOVA with multiple comparisons using the Tukey method indicated that when the impressions of a garment and textile pattern were different, the impression of the garment with that textile pattern was closer to the impression of the textile pattern. When the impressions of a garment and textile pattern were similar, the impression of the garment with that textile pattern was emphasized compared with the unpatterned garment. Our data indicate that the textile pattern has a more significant effect on the garment impression than the garment style. Thus, both the textile pattern and garment style affect the impression of a garment.

The adoption of a simplified nutrition label, called "a nutrition score (Nutri-score¹)", within the EU has been considered in recent years. First, we examine consumers’ decision-making processes when purchasing food. Next, we examine the results of the Consumer Affairs Agency’s questionnaire about how consumers attach importance to nutrition label information. ResultResultResultResult of thisthis this, even if the percentage of correct answers to the problem of reading an energy display was low and it attached it for whether being "Liking to refer to the rate of a nutrition labeling" from the result before long.

we consider whether the NB merch gave priority to what kind of element, and those to whom consumers have purchased the point that name recognition and attractiveness to consumers are suitable, and the private brand has purchased, and how a private brand will spread from now on from the existing literature and a questionnaire. We think that though it is budget increases, consumers' evaluation goes up a private brand from a rise in name recognition, availability, etc. about quality from the above thing. This demonstrates that the strengths of private brands lie in necessities with high purchase frequency, being a category in which consumers cannot easily differentiate merchandise. The demand for private brand food may increase from now on.

We have modelled the effect of the lockdown during the first wave of COVID-19. We used SEIR type of model with a certain time lag between infection and becoming infectious. Firstly we confirmed the timing of the change of the coefficient of infection, growth rate of confirmed cases, corresponds to the change of mobility index, and secondly we assume the change of the coefficient of infection, activity index β (analogous to R₀) and fit the parameter to reproduce the actual number of confirmed cases. Finally, we assume that the activity index β is roughly proportional to the square of the mobility and fit the parameters. The curves in various countries fits reasonably well in any cases, but estimating β from various parameters (including temperature) in the long term remains as an important task.

Sense of agency is the sense that an event is caused by oneself. In the context of man-machine interfaces, it has been proposed that the prediction error of a sensory outcome decreases the sense of agency. We assume that the information content of the sensory response represents the prediction error and thus explains the extent of sense of agency. We use the negative free energy as the information content and formulate the extent of sense of agency as a function of prediction error and sensory precision. The model predicts an interaction effect between the prediction error and sensory precision on the sense of agency. We conducted an experiment through a mouse-clicking task with participants. Between single cue and multiple cues, sensory responses are compared with respect to the effect of response delay as a prediction error on participants’ reported sense of agency value. According to the cue integration model, multiple cues provide more temporal precision of sensory response than a single cue. The results of the experiment supported the interaction effect predicted by the proposed free energy model. Therefore, negative free energy works as a mathematical index of sense of agency.

The function and performance of cloud connected products such as AI speakers are continuously updated over time. Such updates are based on the user’s exploration of unknown functions. Apter’s reversal theory proposed a mental condition termed the paratelic mode in which one acts to explore the purpose and enjoy certain actions in itself. We assume that the paratelic mode motivates users to explore continuously updated functions of cloud connected products which enables them to make full use of them. In this study, we aim to create a mathematical model that can explain the paratelic mode. We propose a model that explains the condition of the paratelic mode by integrating two principal motivation theories: Apter’s reversal theory and Berlyne’s optimal arousal level (OAL). We mathematically formulate the model by applying the Bayesian information gain as an index of arousal. By analyzing the model, we predict two hypotheses: a) when OAL is low, the lower the uncertainty, and the more likely it is that the paratelic mode is achieved, and b) when OAL is high, the higher the uncertainty, the more likely it is that the paratelic mode is achieved. The experimental result of our previous study using an AI speaker supported the former hypothesis. In this study, we verify the latter hypothesis by conducting an experiment using two AI speakers with different uncertainties. The results showed that when OAL is assumed to be high, users are more likely to be in the paratelic mode for an AI speaker which was subjectively evaluated to have higher uncertainty.

The aim of this study is to analyze the impression provided by a color image and the color information contained in the image. To process a large number of color images, we used a method of extracting representative colors from an image based on pixel information. In this study, we developed a method to combine image recognition technology using deep learning with representative color extraction technology, and analyzed the impression and color characteristics of design for store interior images. We collected color images of store interiors along with their tagged keywords from the web. Representative colors were extracted via a method using region division by deep learning. Furthermore, we analyzed the characteristics of the representative colors included in the corresponding images for the keywords "natural," "modern," and "cute," which were frequently used in the collected data to express the impression of the store design. The tendency of color features was analyzed for each image. For example, in "natural" designs, there were many colors close to yellowish green, which were associated with objects such as "plant" and "tree". Such knowledge is important for design support, that is, knowledge of the color to be incorporated into the design, as well as of methods to incorporate that color.

Japan is currently witnessing an increase in the number of individuals with dementia. According to a survey, there were 4.62 million people with dementia in Japan in 2012 and more than 7 million in 2025. The early detection of dementia is crucial to delay the progression of symptoms. As such, in our laboratory, we are developing a dementia screening tool using a character-input-type brain.computer interface (BCI). In this study, the spelling-type-BCI is used to analyze and verify electroencephalograph (EEG) data obtained in the frequency band. We aim to clarify how EEG characteristics differ between healthy subjects and those with mild cognitive impairment (MCI). As a result, we observed that a high possibility exists that there is a difference in the mean value of β/α and the generation rate of θ waves among healthy subjects, patients with MCI, and patients with Alzheimer's dementia. This difference can likely be attributed to our consideration of β/α as an index of the degree of concentration associated with cognitive decline and θ waves as the characteristics of the EEGs of patients with dementia. Based on these results, measuring β/α and θ waves could lead to the early detection and diagnosis of dementia.

Early diagnosis is important in the treatment of dementia; however, many dementia patients are resist seeking medical attention. In our laboratory, we are developing a dementia screening tool using the P300-based Spelling-Brain-Computer Interface (Spelling-BCI) to facilitate early dementia diagnosis. By estimating the results of neuropsychological examinations that must be performed by a specialist with BCI, we consider that an easy cognitive function test with Spelling-BCI can be realized. Multiple regression analysis was performed using the features obtained from the Spelling-BCI and the age of the subjects, and Mini-Mental State Examination (MMSE), Japanese Version of Montreal Cognitive Assessment (MoCA-J), and Frontal Assessment Battery (FAB) scores were estimated. In the multiple regression analysis, variable selection was performed using the forward-backward stepwise selection method, and data exceeding the 95% confidence interval of the estimation error were excluded. As a result, the adjusted R-squared exceeded 0.95 in the estimation model of each neuropsychological examination. Therefore, the experimental results suggest that neuropsychological examinations can be estimated using the Spelling-BCI.

We have developed a measuring garment for the shape of a women’s upper body for personalized garment design. A stretch sensor with a linear relationship between the elongation and capacitance was used. Twenty one measurement locations were determined based on the required measurements to make a basic pattern. Twenty one sensors were sewn on a long-sleeved T-shirt corresponding to the measurement locations. The capacitance of each sensor was measured while a subject was wearing the measuring garment. We estimated the measurements owing to short length of the sensor compared with the measurement gauge. Because there were linear relationships between the measured dimensions by the three-dimensional (3D) scanner and the capacitance values from the sensors at all of the measurement locations, we used the linear regression equations to estimate the measurements. An upper garment pattern for the subject was successfully created using the obtained measurements. To confirm the fit of the pattern, a wearing simulation was performed using a 3D apparel simulation system. It fitted well on the subject without wrinkles and redundant ease. Therefore, it is possible to measure a women’s upper body using the proposed measuring garment.

Conventional research on decision support focuses on rational decision-making. However, consumer decision-making is considered ambiguous and varies depending on the situation and context. Therefore, the authors investigated the differences in selection results depending on the usage of products. Specifically, the authors examined the variation in the choice of earphones according to place and situation of use. Multiple comparisons showed that the number of types of earphones selected did not differ significantly depending on the place of use, but differed greatly according to the usage situation. The number of earphones selected varied depending on the place and situation of use. In particular, it was suggested that the use of earphones had a significant influence on choice, which may lead to decision support for product selection.

Nonverbal information can be used to convey emotions. In some cases, we can read emotions from changes in the other person’s facial expressions. However, the facial expression is not transmitted to other people in some cases. To address this issue, a glasses-type device with pseudo eyebrows that change eyebrow’s thickness and angle was used in the previous research to facilitate the transmission of emotions to others. The device is charging type and cannot be used easily and cannot work for one day. In this study, we use “SUGO-MIMI,” a lightweight device that does not require a power source to extend facial expressions by eyebrow movements. The SUGO-MIMI extends the eyebrow movements by connecting the eyebrows to thin plates imitating cat ears attached to the headband with wires and linking the eyebrows’ movement to the plates. The eyebrow movement is extended. Thus, the emotions of joy, anger, and sorrow can be emphasized. As a result of analyzing the facial expressions of the subjects that watch videos of a person who wears the SUGO-MIMI using the Microsoft Face API, we obtained that the SUGO-MIMI can amplify some emotions and that women tended to have more positive impressions of the SUGO-MIMI than men.

To examine the impression including the visual deliciousness with label color in PET bottled black tea, the impressions of the combination between label color and color of black tea were measured using subjective evaluation and SD method with varying the way of its combination. For the stimulus, they were three types of black tea and eleven types of label color rapped in the PET bottles. In the results, the common black tea and yellowish black tea are evaluated visually delicious in reddish and yellowish colored label of PET bottles, whereas the value of visual deliciousness in the reddish black tea is lower than the others. Overall the results are a similar tendency to those of previous studies. In conclusion, it was shown that both the color of the tea beverage and the color of the packaging label have an effect on the evaluation of the taste and impression.

Kawaii is a Japanese cultural uniqueness that attracts attention around the world. In this study, we focused on pink as a typical kawaii color. Four pink colors were selected and used in our questionnaire to collect data about most kawaii and most favorite pink colors as well as the behavior in using pink products including clothing and makeup. From the questionnaire results, we obtained the relationship between the most kawaii and/or the most favorite pink, and behavior in using pink products. Furthermore, we interpreted how fashion trend influenced to the results. Finally, we obtained tendencies of the pink colors for the most kawaii and the most favorite as well as their relationship with fashion trend, which should be useful for fashion product designs.

New lighting applications aspire to provide more benefits —than simply allowing vision— such as improving mood, sleep quality, and daily performance. 3D printing and laser cutting technologies found an application in the design of luminaries with very complicated artificial structures where the creation of unique light patterns is achieved. Computer-based, lighting simulation and assessment tools can assist in the creation and analysis of the lighting quality of space. However, these outcomes depend on the lighting designer’s knowledge and experience. It is necessary to develop methods to quantitatively evaluate people’s feelings and preferences toward such stimuli and to implement those emotional qualities into the lighting of space. This introductory study proposes a lighting design approach where the elements of light patterns, such as the direction of light, the beam pattern, the color temperature of light and the symmetry of light distribution, are assumed as design elements. The second assumption was that the lit lamp has a different appearance than the non-lit lamp, thus these can be understood as two different products requiring two different Kansei scales in order to evaluate the emotional impressions they give to the perceiver. In this context, the study had two goals: (a) to apply Kansei Engineering for the classification of lit and non-lit wall sconces based on the people's emotional perceptions; (b) to implement the people's emotional preferences of wall sconce's lighting into the selection of a wall sconce that creates an intended space mood in the dark and an invisible effect in daylight.

Daily sleep monitoring is necessary to make potential patients with sleep apnea syndrome aware of their respiration state during sleep. Although it is desirable to have an unconstrained system for daily monitoring in a home environment, the amplitude of respiration measured by an unconstrained sensor varies depending on the participants’ properties and the recumbent positions. In this study, we propose an algorithm for classifying the respiration state by extracting the respiratory cycle in the signal measured by an unconstrained respiration measurement system as a feature. We confirmed that the respiratory cycle obtained using autocorrelation has different characteristics between the breathing/respiratory arrest periods. By analyzing the respiratory cycle in the biological signal, it was found that the method was resilient to amplitude changes due to differences in the participants’ properties and in the recumbent positions. As a result of cross-validation to evaluate the proposed algorithm, the evaluation indices are all high, and it is confirmed that the algorithm is resilient to variations in the participants’ properties and in the recumbent positions.

In Japan, stroke is the second leading cause of long-term care. Therefore, patients with stroke need long-term rehabilitation. The Functional Independence Measure (FIM) is a measure of activities of daily living in stroke patients, which is a reliable physical therapy assessment that can be used in a multidisciplinary setting. However, the FIM scores are assessed by experienced nurses and difficult to assess accurately at home. In this study, we developed a system to estimate the FIM score easily at home using angular velocity from the target bodily movements using gyrosensors. Here, there are two types of target bodily movements: rising from a bed and sit-to-stand (STS) movement. To consider the instability of the two target bodily movements, the sum of vibrations was calculated from the acquired angular velocity time series data as features. We performed a Gaussian process regression analysis with the features as explanatory variables and the FIM motor score assessed by the nurse as an objective variable. Finally, we performed a series of experiments to evaluate the proposed method and calculated the root mean square error (RMSE) between the estimated FIM motor score and that assessed by the nurse. As a result, the RMSE with the lowest result was 10.42.

In our previous study, the effects of tactile feedback on the affective evaluation of switch sounds were examined through auditory and tactile evaluation experiments using 15 switches, and it was shown that these effects could be modeled using multiple regression analyses. However, the reason why the effects can be modeled by multiple regression analyses remains unanswered. In this study, extended experiments were conducted using 25 switches. In the auditory evaluation experiment, using 26 adjective pairs on 7-point category scales, two groups consisting of 22 subjects each participated as operators and listeners, respectively. In the tactile evaluation experiment, using 16 adjective pairs, another group of 52 subjects participated. The results of both experiments were analyzed, separately, using factor analyses, with the three factors of potency, evaluation, and activity being extracted for both experiments. The factor scores of the operators were modeled using multiple regression analyses with the factor scores of listeners and tactile evaluations. Another modeling was conducted based on Bayes’ theorem, wherein the factor scores of listeners and operators were regarded as prior and posterior distributions, respectively, with the tactile factor scores being regarded as likelihood. The fact that these two models show almost the same estimation performance suggests that the effects of tactile feedback on the affective evaluation of switch sounds are modeled well by the multiple regression analysis because Bayesian multisensory integration is the origin of the cross-modal effects. This can be understood by the fact that the estimation equations of both models are essentially the same.

The prevalence of mood disorders is increasing worldwide, making mental health self-care necessary. One possible solution to address this need is the use of wearable technology that monitors and records moods. In this study, we attempted to (1) estimate the emotional states of joy, anger, and neutral from blood volume pulse intervals and (2) evaluate the potential applicability of blood volume pulse-based mood mapping in wearable devices. The pulse wave data available on the Massachusetts Institute of Technology (MIT) Affective Computing Group website were used. Acceleration pulse waves were analyzed using second-order differential calculus. Mean NN interval, standard deviation of NN intervals, and coefficient of variation of RR intervals were analyzed as emotional features, based on which a three-state classification model was created via linear discriminant analysis. The classification accuracy at the pulse wave measurement time of 30 seconds was 57%, whereas that at 100 seconds was 53%. Our findings indicate that mood estimation using acceleration pulse wave analysis has potential application in wearable technology for mental health self-care and warrant further research to strengthen the data.

With the current spread of the Internet, information is being collected in various ways, such as watching videos. However, the basis of information collection is reading sentences, with books as typical media. Books comprise both print books and e-books, but many people find that paper-based books are easier to read than e-books. Therefore, a commonly used learning method is reading print books. However, reading on paper is easily affected by the reader’s situation, such as the surrounding brightness, environmental sound, and posture. Although there are many environmental factors, in this experiment, we focused on the environmental lighting. The purpose of this study is to determine an environment where one can concentrate on reading. For that purpose, we evaluate and determine the flow, which is the feeling of being immersed, via the number of blinks, head movement, and subjective information. It was found that local lighting results in greater head acceleration and angular velocity values, but the number of pages to be read is greater and the reading experience time is shorter. Therefore, although the experiment was limited to five subjects, it can be said that an environment is more suitable for reading when local lighting is used.

Studies investigating the association between emotions and biological information, such as breathing, pulse wave, heartbeat, voice, and facial expression, have suggested that many of them can estimate emotions. However, few studies have linked walking to emotions. In this study, we used optical motion capture to evaluate walking in humans in four emotional states, namely happy, angry, sad, and relaxed, and examined differences for each emotion. We found that walking in happy and sad states had unique features; whereas that in angry and relaxed states, which had few unique features. In other words, happy and sad states are easy to interpret, whereas angry, and relaxed states are a challenge. However, humans can distinguish between angry and relaxed walking. Therefore, in addition to gait, humans may observing other factors, such as how force is applied and the center of gravity.