Tetsuro Aoto

Systematic study for “kawaii” products (the second report) -commpmrison of “kawaii” colors and shapes -

In the 21st century, the Kansei values of industrial products are considered very important. In this study, we focused our attention on kawaii as a Kansei value for future industrial products, and analyzed kawaii attributes systematically to construct kawaii products. After performing a simple experiment using magnets, we performed another experiment with kawaii colors and shapes in two-dimensional plane and three-dimensional virtual space.

Kawaii Rules: Increasing Affective Value of Industrial Products

The Japanese word “Kawaii,” which represents a kansei/affective value, has such positive meanings as cute, lovable, and small. In the 21st century, the kansei/affective values of industrial products are becoming very important. However, since few studies have focused on kawaii attributes, we systematically analyze kawaii products themselves: the kawaii feelings caused by shapes, colors, sizes, and texture and tactile sensation caused by materials of those products. In this chapter, we introduce our experimental results for abstract objects in virtual environments and describe interesting tendencies for the visual attributes of kawaii, including thier shapes, colors, and sizes. We present these tendencies as kawaii rules.

Systematic study of kawaii products: Relation between kawaii feelings and attributes of industrial products

In Japan, the cute aesthetic is abused by many organizations and for many purposes including police mascots, and warning signs for dangerous areas. Although using cute to motivate and inform might seem strange, cute does offer potential. Dr. Cheok and his team at the National University of Singapore argued that Japanese ‘kawaii’ embodies a special kind of cute design, which reduces fear and makes dreary information more acceptable and appealing. Various Japanese kawaii characters such as Hello Kitty and Pokemon have become popular all over the world. However, since few studies have focused on kawaii attributes, we systematically analyze the kawaii interfaces themselves: kawaii feelings caused by such attributes as shapes, colors, and materials. Our aim is to clarify a method for constructing a kawaii interface from the research results. Kawaii might be one important kansei value for future interactive systems and industrial products of Asian industries. We previously performed experiments and obtained interesting tendencies about such kawaii attributes as shapes, colors, and sizes. Although questionnaires are the most common form of kansei evaluation, they suffer from such demerits as linguistic ambiguity, the possibility of mixing the intensions of experimenters and/or participants into the results, and interruption of the system’s stream of information input/output. Thus, to compensate for these demerits, we examined the possibility with biological signals. In this article, these experiments and their results are outlined.© 2010 ASME

Measurement of Wakuwaku Feeling of Interactive Systems Using Biological Signals

To evaluate the kansei values of interactive systems, subjective evaluation methods such as questionnaires are commonly used, even though they have some drawbacks such as linguistic ambiguity and interfusion of experimenter and/or participant intention to the results. We began our research to objectively evaluate interactive systems by quantifying sensations using biological signals to redeem the above questionnaire drawbacks. We utilize biological signals to estimate participants’ feelings of relaxation, comfort, and excitement, which are considered positive sensations. However, relaxation and comfort are considered static compared with dynamic feelings such as excitement. We focus on a positive and dynamic feeling called “wakuwaku” in this chapter, and construct various systems to evaluate the kansei values used to derive wakuwaku feelings using biological signals, in order to clarify the relationship between the wakuwaku feeling and biological signals. In addition, we derive a kansei model of interactive systems using biological signals to objectively evaluate their wakuwaku degree.

A proposal of wakuwaku model of interactive system using biological signals

To evaluate the Kansei of interactive systems, such subjective evaluation methods as questionnaire are usually used, even though they have some demerits. We objectively evaluate a system by quantifying its sensation of excitement using biological signals when one feels something interesting, because such signals can supplement the demerits of questionnaires. Thus, by focusing on excitement to derive a wakuwaku model, in this study we built various wakuwaku systems and evaluated them. Based on the biological signals measured during evaluation experiments, we derived a model using a neural network, and concluded that we can evaluate a system by the derived model.Copyright

The analysis of near-miss cases using data-mining approach

We applied the data mining technique to medical near-miss cases collected by a foundation related to the Japanese Health, Labor and Welfare Ministry, and extracted information such as pairs of confusing medicines, the cause of near-miss cases in some situations, which cannot be obtained by simple aggregation calculations and descriptive statistics. We also introduce the results of text mining applied to the free-description data regarding the backgrounds and causes of near-miss cases and their counter measures.

Application of Data Mining and Text Mining to the Analysis of Medical near Miss Cases

Not only the side effects of medicines themselves, but also their abuse, namely the lack of safety in drug usage, can cause serious medical accidents. The latter applies to the case of the mix-up of medicines, double dose or insufficient dose. Medical equipments can also cause accidents because of wrong treatment, such as wrong input to equipments and wrong power-off. In order to avoid such accidents, it is necessary to investigate past cases to identify their causes and work out counter measures. Medical near-miss cases caused by wrong treatment with the medicines or the medical equipments are strongly related to medical accidents that occur due to the lack in safety of usage. Medical near-miss cases are incidents, which could be medical accidentsavoided owing to certain factors, and happen more frequently than medical accidents. Incorporating Heinrich’s law, which shows the tendency of frequency and seriousness of industrial accidents, we estimate that near-miss cases happen three hundred times per one serious medical accident or thirty minor accidents. This can be interpreted as there being many causes of medical accidents, most of which are eliminated by certain suppression factors, which lead to near-miss cases. The rest of the causes lead to medical accidents. From this perspective, we can expect that both medical accidents and near-miss cases originate from the same type of causes, which suggests that the analysis of data on near-miss cases is valid to investigate the cause of medical accidents, since their occurrence frequency is much larger than that of medical accidents. For the reasons stated above, we analyze the data of medical near-miss cases related to drugs and medical equipments, which have been collected in previous years to determine the root cause of medical accidents caused by the neglect of safety of usage. Though simple aggregation calculations and descriptive statistics have already been applied to them, the analyses are too simple to extract sufficient information such as pairs of medicines that tend to be confused, and the relationships between the contents of incidents and the causes. To realize such analyses, we utilize data mining techniques such as decision-tree and market-basket analysis, and text-mining techniques such as the word linking method. The related works analyzing medical databy utilizing natural language processing ormachine learningwere introduced by Hripcsak et al (Hripcsak et al., 2003), who suggested the framework todetect events such as medical errors or adverse outcome.Recently,