• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.5
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• pp.1-4
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• 2014

In recent years, requests for automotive seat comfort are increasing. An important issue of them is long-term seat comfort. Until now, the study for long-term seat comfort has been studied mainly using driver's questionnaire, changing adrenalin and electromyography. Actually the results and methodologies of them are difficult to apply to seat development and design because of money and time required. In this study, we developed Seating Feel Curve for seat comfort evaluation and a long-term seat comfort evaluation which can be applied to the development of seat comfort using seat support.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.4
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• pp.442-449
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• 2013

This study was to compare a domestic comfort shoes to the popular import(SAS$^{(R)}$) to gain a better understanding in biomechanical characteristics for shoe design for the elderly. For each shoe type, morphologic dimensions, shock absorption, and flexibility were assessed. From subjects(n = 20, $72.4{\pm}5.5$ years, $67.7{\pm}7.9$ kg), mean peak pressure(MPP) and contact area(CA) at the plantar surface were analyzed. The domestic shoes reflecting anatomic contour of the plantar surfaces of Asians resulted in wider CA(by 30.4 $cm^2$), higher shock absorption(by 2.4%) and stiffness(by 0.5 N/mm) than the import. With the domestic shoes, significantly less MPPs were found at the forefoot(by 42~49%) regions(p < .05) and higher CA was noted additionally at hallux and lessor toes(by 26~63%). More anatomically-contoured insole and favorable mechanical characteristics may help reduce the plantar pressures more effectively and more evenly, especially across the central forefoot and midfoot regions of the plantar surface, especially for the design of the comfort shoes for the elderly.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.447-454
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• 2022

In the era of the 4th industrial revolution, we are living in a flood of information. It is very difficult and complicated to find the information people need in such an environment. Therefore, in the flood of information, a recommendation system is essential. Among these recommendation systems, many studies have been conducted on each recommendation system for movies, music, food, and clothes. To date, most personalized recommendation systems have recommended clothes, books, or movies by checking individual tendencies such as age, genre, region, and gender. Future generations will want to be recommended clothes, books, and movies at once by checking age, genre, region, and gender. In this paper, we propose a recommendation system that recommends personalized clothes and food at once according to the user's emotions and weather. We obtained user data from Twitter of social media and analyzed this data as user's basic emotion according to Paul Eckman's theory. The basic emotions obtained in this way were converted into colors by applying Hayashi's Quantification Method III, and these colors were expressed as recommended clothes colors. Also, the type of clothing is recommended using the weather information of the visualcrossing.com API. In addition, various foods are recommended according to the contents of comfort food according to emotions.