Barrier-free films enhance accessibility to audiovisual image contents by providing specific information on screen and through sound so that people with vision or hearing loss can receive the same amount of information as those without disabilities and immerse themselves in the audiovisual images. This study pays attention to barrier-free audiovisual contents in relation to the cultural civil rights of people with vision or hearing loss in South Korea. While institutional efforts have been made in the 2010s to improve the access to audiovisual media of people with vision or hearing loss, the goal of enabling people with vision or hearing loss to fully enjoy all audiovisual contents at a level equal to the non-disabled has not yet been realized. Amid the lingering conflict between disabled groups and multiplexes that has lasted years, the global video streaming service Netflix has aggressively threatened the dominance of local multiplexes with the launch of its Korean service. As Netflix, which is subject to U.S. regulations guaranteeing the rights of people with vision or hearing loss, has produced original dramas and movies involving Korean production teams, the cultural civil rights discourse of the disabled has transitioned to the issue of the rights of cultural consumers crossing national borders in the era of globalization. Changes in the media environment raise the issue of civil rights guarantees in which disabled people enjoy the right to simultaneously watch movies and comment on movies by participating in a common discourse, equally with non-disabled people. The "right to be part of the audience for Korean cinema" for Korean deaf people, which has long been neglected, should also be considered as a cultural civil right that crosses the boundaries of language, nation and disabilities. This essay examines the current issues surrounding the right to cultural entertainment of people with vision or hearing loss in South Korea in conjunction with the contemporary trend of rapid changes in the media environment and the global spread of the movement for cultural civil rights of people with disabilities, and suggests the need for visual culture studies to take a serious step toward disability studies.
Objective : The purpose of this study was to confirm the characteristics and quality of a single-subject research that conducted interventions to improve activities of daily living (ADL) in stroke patients. Methods : 'Stroke,' 'activities of daily living,' and 'single-subject studies' were searched as keywords among papers published in the last 15 years between 2009 and 2023 among Research Information Sharing Service, DBpia, and e-articles. A total of nine papers were examined for the characteristics and quality before analysis. Results : The independent variables applied to improve ADL included constraint-induced therapy, mental practice for performing functional activities, virtual reality-based task training, subjective postural vertical training without visual feedback, bilateral upper limb movement, core stability training program, traditional occupational therapy and neurocognitive rehabilitation, smooth pursuit eye movement, neck muscle vibration, and occupation-based community rehabilitation. Assessment of Motor and Process Skills was the most common evaluation tool for measuring dependent variables, with four articles, and Modified Barthel Index and Canadian Occupational Performance Measure were two articles each. As a result of confirming the qualitative level of the analyzed papers, out of a total of nine studies, seven studies were at a high level, two at a moderate level, and none were at a low level. Conclusion : Various types of rehabilitation treatments have been actively applied as intervention methods to improve the daily life activities of stroke patients; the quality level of single-subject studies applying ADL interventions was reliable.
According as the automation of clerical work(OA ; Office Automation) develops, the use of VDT(Visual or Video Display Terminal) is increasing suddenly. But, in proportion to the spread of office automation(OA tendency), the self-conciousness syptom attendant upon the work is appearing also (Kim, Jung Tae, Lee, Young Ook, 1990). The apparatuses of office enable the clerical workers to be convenient and perform mass businesses. But, they are increasing the opportunity to be exposed to VDT syndrom, techno stress, computer terminal disease, pain by muscle strain(RSI), bradycausia of noise nature, and electromagnetic waves, etc. which are referred to as the new type of occupational diseases to the workers. It is the real situation that the workers to use VDT is complaining of the physical inconvenience sense in the recent newspaper and literature, it is the point of time that the sydrome to come from VDT use and computer terminal disease, etc. must be classified into the occupational disease(Lee, Kwang Young 1990, Lee, Kyoo Hak 1990, Lee, Won Ho 1991, Lee, Si Young 1991, Lee, Joon 1991, Choi, Young Tae 1991, Heo, Seung Ho 1989). In addition, it is the real situation that the scientifitic study result about the scope that electromagnetic waves has influence on the human body has not been suggested yet, and criticism on the stable exposure permission standard about electromagnetic waves to be emitted from VDT and on the problem in the health about electromagnetic waves is continuing. (IEEE Spectrum, 1990). In addition according to the experience of nursery business of industry field, it is the real situation that the patients who consult complaining of physical and mental inconvenience sence, among the users of apparatus of office automation, are reaching 10% of the patients coming to doctor's room. Therefore, it is necessary to confirm the self-consciousness symptom that the clerical workers complain of multilaterally with the actual state examination about the use of the apparatuses of offices automaton. Thus, this study was tried as th basic data for the cosultation and education for the maintenance and furtherance of the health of workers as the nurse of industry field, by confirming the contents of self-consciousness symptom attendant upon the use of the apparatus for office outomation making the financial institution in which the spparatus for office automation in most frequently used as the subject, and by examining whether there is the difference according to the subject of study, the data were collected, by using the questionnaire method, making 200 workers who consented to the study participation as the subject, among the persons who have spent over 3 months since they used the apparatuses for office automation and didn't receive the treatment in hospital due to the clerical disease for recent 3 years. The period of data collection was from Oct. 9, 1991 to Oct. 12. As for the measurement instrument about the complaint if self-consciousness symptom attendant upon the use of apparatuses fo office automation, the question item on the complaint symptom of health problem attendant upon the treatment of VDT that Kim(1991) developed and on CMI health problem and the question items on the fatigue degree due to industry were used by previous examination to 25 persons. Collected data were analyzed with the statistical method such as percentage, arithmetic mean, Person correlation coeffient, Kai square verfication, t-test, ANOVA, etc. by using SPSS/PC+ program, and the result is as follows : 1. The self-consciousness symptom that the clerical workers complained of most frequetly appeared high in 'My eyes are tired'(99.4%), 'I feel fatigue and weariness'(99.4%), 'I feel that my head is heavy5(90.0%), 'eyesight fell'(88.8%), 'I have a stiff neck'(88.8%), 'I fell pain in the shoulder'(85.0%), 'I feel cold and painful in the eyes'(76.9%), 'I feel the dry sense of eyeball'(76.2%), 'My nerves are edgy, and I an fretful, (75.6%), 'I feel pain in the waist'(73.2%) and 'I fell pain in the back'(72.8%). It emerged that the subject use the apparatuses for office automation complained of self-consciousness symptoms related to visual symptoms and musculoskeletal symptoms. 2. As for the general feature of examination subjects, the result to see the distribution by classifying into sex, age, school career, use career of apparatuses for office automation, skillfulness degree of the use of apparatus for office automation, use hours of the apparatuses for office automation per 1 day, type of business of the apparatus for office automation, rest hours during the use of apparatus for office automation, satifaction degree of business of office automation, and work circumstance, etc. emerged as follows : As for the sex of subjects, the distribution showed that men were 58.8% and women were 41.3%, Age was average 26.9. As the distribution of school career, the distribution showed that4below the graduation of high school' was 58.8%, 'graduation from junior college-university' was 35.0%, and 'over graduate school' was 6.3%. In the question to ask the existence or non-existence of experience of health consultation in connection with the work of office automation, the response that I had the consultation exprience and I feel the necessity emergerd as 90.1% And, the case that the subject who didn't wear the glasses or lens before using the OA apparatus wear glasses or lens after using OA apparatus emerged as 28.3% of whole. As for the existence or non-existence of use career of OA apparatus, the case under 3 years was highest as 52. 7%. As for the skillfulnness degree about the use of apparatus for office automation, most of them are skillful with the fact that 'common' was 44.4%, 'skill' was 42.5%, and 'unskillful' was 13.1% As for the use average hours of the apparatus for office automation per 1 day, the distribution showed that the case under 3-6 hours was 33.1%, the case under 6-9 hours was 28.1%, the case under 3 hours was 30.6%, and the case over 9 hours was 8.1% Main OA business and the use hours for 1 day showed in the order of keeping and retrieval, business of information transmission(162min), business of information transmission(79.3 min), business of document framing(55.5 min), and business of duplication and printing(25.4min). as for the rest during the use of apparatus for affice automation, that I take rest occasion demands the major portion, but that I take after completing the work emerged as 33.8%. Though the subiness gets to be convenient by the use of the apparatus for of office automation, respondents who showed the dissatisfaction about the present OA business emergd high as 78.1%. The work circumstances of each office was good with the fact that the temperature of office was 21.8, noise was average 42.7db, and the illumination was average 364.4 lx, in the light of ANSi/HFS 100 Standard. 3. Sight syptom, musculoskeletal symptom, skin and other symptoms showed the significant difference according to the extent of skillfulness of the apparatus for office automation. All the symptoms exept skin symptom showed the difference according to the use hours of the apparatus for office automation. All the question items exept the sytoms of digestive organs and the rest hours during the apparatus for office automation showed the signicant difference. The question item which showed the signicant difference from the satisfaction degree of present OA business showed the significant difference from all the question item classified into 6 groups. But, age and school career didn't significant difference from the complaint of any self-consciousness symptoms.
. In conclusion, the self-consciousness symptoms of the subjects to use OA apparatus appeared differently, according to sex distiction, skillfull degree of OA apparatus, use hours of OA apparatus, the rest hours during th use of OA apparatus, and the satiafaction degree of persent business. Therefore, it is necessary that the nurse in the inuctry field must recognize to receive the education about the human technological physical condition which is most proper for te use of OA apparatus and about the proper rest method until they get accustomed to the use of OA apparatus. In addition, the simple exercise relax the tention of muscle due to the repetitive simple movement, and the education for the protection of eyesight are necessary.
Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.
Recommender system recommends the items expected to be purchased by a customer in the future according to his or her previous purchase behaviors. It has been served as a tool for realizing one-to-one personalization for an e-commerce service company. Traditional recommender systems, especially the recommender systems based on collaborative filtering (CF), which is the most popular recommendation algorithm in both academy and industry, are designed to generate the items list for recommendation by using 'overall rating' - a single criterion. However, it has critical limitations in understanding the customers' preferences in detail. Recently, to mitigate these limitations, some leading e-commerce companies have begun to get feedback from their customers in a form of 'multicritera ratings'. Multicriteria ratings enable the companies to understand their customers' preferences from the multidimensional viewpoints. Moreover, it is easy to handle and analyze the multidimensional ratings because they are quantitative. But, the recommendation using multicritera ratings also has limitation that it may omit detail information on a user's preference because it only considers three-to-five predetermined criteria in most cases. Under this background, this study proposes a novel hybrid recommendation system, which selectively uses the results from 'traditional CF' and 'CF using multicriteria ratings'. Our proposed system is based on the premise that some people have holistic preference scheme, whereas others have composite preference scheme. Thus, our system is designed to use traditional CF using overall rating for the users with holistic preference, and to use CF using multicriteria ratings for the users with composite preference. To validate the usefulness of the proposed system, we applied it to a real-world dataset regarding the recommendation for POI (point-of-interests). Providing personalized POI recommendation is getting more attentions as the popularity of the location-based services such as Yelp and Foursquare increases. The dataset was collected from university students via a Web-based online survey system. Using the survey system, we collected the overall ratings as well as the ratings for each criterion for 48 POIs that are located near K university in Seoul, South Korea. The criteria include 'food or taste', 'price' and 'service or mood'. As a result, we obtain 2,878 valid ratings from 112 users. Among 48 items, 38 items (80%) are used as training dataset, and the remaining 10 items (20%) are used as validation dataset. To examine the effectiveness of the proposed system (i.e. hybrid selective model), we compared its performance to the performances of two comparison models - the traditional CF and the CF with multicriteria ratings. The performances of recommender systems were evaluated by using two metrics - average MAE(mean absolute error) and precision-in-top-N. Precision-in-top-N represents the percentage of truly high overall ratings among those that the model predicted would be the N most relevant items for each user. The experimental system was developed using Microsoft Visual Basic for Applications (VBA). The experimental results showed that our proposed system (avg. MAE = 0.584) outperformed traditional CF (avg. MAE = 0.591) as well as multicriteria CF (avg. AVE = 0.608). We also found that multicriteria CF showed worse performance compared to traditional CF in our data set, which is contradictory to the results in the most previous studies. This result supports the premise of our study that people have two different types of preference schemes - holistic and composite. Besides MAE, the proposed system outperformed all the comparison models in precision-in-top-3, precision-in-top-5, and precision-in-top-7. The results from the paired samples t-test presented that our proposed system outperformed traditional CF with 10% statistical significance level, and multicriteria CF with 1% statistical significance level from the perspective of average MAE. The proposed system sheds light on how to understand and utilize user's preference schemes in recommender systems domain.
Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.
The current smart lighting is shaped to offer the lighting environment suitable for current context, after identifying user's action and location through a sensor. The sensor-based context awareness technology just considers a single user, and the studies to interpret many users' various context occurrences and conflicts lack. In existing studies, a fuzzy theory and algorithm including ReBa have been used as the methodology to solve context conflict. The fuzzy theory and algorithm including ReBa just avoid an opportunity of context conflict that may occur by providing services by each area, after the spaces where users are located are classified into many areas. Therefore, they actually cannot be regarded as customized service type that can offer personal preference-based context conflict. This paper proposes a priority-based LED lighting control system interpreting multiple context conflicts, which decides services, based on the granted priority according to context type, when service conflict is faced with, due to simultaneous occurrence of various contexts to many users. This study classifies the residential environment into such five areas as living room, 'bed room, study room, kitchen and bath room, and the contexts that may occur within each area are defined as 20 contexts such as exercising, doing makeup, reading, dining and entering, targeting several users. The proposed system defines various contexts of users using an ontology-based model and gives service of user oriented lighting environment through rule based on standard and context reasoning engine. To solve the issue of various context conflicts among users in the same space and at the same time point, the context in which user concentration is required is set in the highest priority. Also, visual comfort is offered as the best alternative priority in the case of the same priority. In this manner, they are utilized as the criteria for service selection upon conflict occurrence.
Objective : It has been proposed that cognition and related aspects of mental functioning are decreased in depression as well as in alcoholism. The objective of the study was to compare behavioral side effects of paroxetine and amitriptyline in depressed patients accompanied by alcoholism. The focused comparisons were drug effects concerning psychomotor performance, cognitive function, sleep and daytime sleepiness during the first 2 weeks of treatment. Methods : After an alcohol detoxification period(3 weeks) and a washout period(1 week), a total of 20 male inpatients with alcohol use disorder (DSM-IV), who also had a major depressive episode(DSM-IV), were treated double-blind with paroxetine 20mg/day(n=10) or amitriptyline 25mg/day(n=10) for 2 weeks. All patients were required to have a scare of at least 18 respectively on bath the Hamilton Rating Scale far Depression(HAM-D) and Beck Depression Inventory(BDI) at pre-drug baseline. Patients randomized to paroxetine received active medication in the morning and placebo in the evening whereas those randomized to amitriptyline received active medication in the evening and placebo in the morning. All patients performed the various tasks in a test battery at baseline and at days 3, 7 and 14. The test battery included : critical flicker fusion threshold for sensory information processing capacity : choice reaction time for gross psychomotor performance : tracking accuracy and latency of response to peripheral stimulus as a measure of line sensorimotor co-ordination and divided attention : digit symbol substitution as a measure of sustained attention and concentration. To rate perceived sleep and daytime sleepiness, 10cm line Visual analogue scales were employed at baseline and at days 3, 7 and 14. The subjective rating scales were adapted far this study from Leeds sleep Evaluation Questionnaire and Epworth Sleepiness Scale. In addition a comprehensive side effect assessment, using the UKU side effect rating scale, was carried out at baseline and at days 7 and 14. The efficacy of treatment was evaluated using HAM-D, BDI and clinical global impression far severity and improvement at days 7 and 14. Results : The pattern of results indicated thai paroxetine improved performance an mast of the lest variables and also improved sleep with no effect on daytime sleepiness aver the study period. In contrast, amitriptyline produced disruption of performance on same tests and improved sleep with increased daytime sleepiness in particular at day 3. On the UKU side effect rating scale, mare side effects were registered an amitriptyline. The therapeutic efficacy was observed in favor of paroxetine early in day 7. Conclusion : These results demonstrated thai paroxetine in much better than amitriptyline for the treatment of depressed patients accompained by alcoholism at least in terms of behavioral safety and tolerability, furthermore the results may assist in explaining the therapeutic outcome of paroxetine. For example, and earlier onset of antidepressant action of paroxetine may be caused by early improved cognitive function or by contributing to good compliance with treatment.
Remotely sensed satellite images can be applied to monitor and obtain land surface information on inaccessible areas. We classified paddy field area in North Korea based on on-screen digitization with visual interpretation using 291 RapidEye satellite images covering the whole country. Criteria for paddy field classification based on RapidEye imagery acquired at different time of rice growth period was defined. Darker colored fields with regular shape in the images with false color composite from early May to late June were detected as rice fields. From early July to late September, it was hard to discriminate rice canopy from other type of vegetation including upland crops, grass, and forest in the image. Regular form of readjusted rice field in the plains and uniform texture when compared with surrounding vegetation. Paddy fields classified from RapidEye imagery were mapped and the areas were calculated by administrative district, province or city. Sixty six percent of paddy fields ($3,521km^2$) were distributed in the west coastal regions including Pyeongannam-do, Pyeonganbuk-do, and Hwanghaenam-do. The paddy field areas classified from RapidEye images showed less than 1% of difference from the paddy field areas of North Korea reported by FAO/WFP (Food and Agriculture Organization/World Food Programme).
Tangible user interfaces have been developed in the area of Human-Computer Interaction for the last decades, however, the applied domains recently have been extended into the product design and interactive art. Tangible User Interfaces are the combination of digital information and physical objects or environments, thus they provide tangible and intuitive interaction as input and output devices, often combined with Augmented Reality. The research developed a design guideline for tangible user interfaces based on key properties of tangible user interfaces defined previously in five representative research: Tangible Interaction, Intuitiveness and Convenience, Expressive Representation, Context-aware and Spatial Interaction, and Social Interaction. Using the guideline emphasizing user interaction, this research evaluated installation in a science museum in terms of the applied characteristics of tangible user interfaces. The selected 15 installations which were evaluated are to educate visitors for science by emphasizing manipulation and experience of interfaces in those installations. According to the input devices, they are categorized into four Types. TUI properties in Type 3 installation, which uses body motions for interaction, shows the highest score, where items for context-aware and spatial interaction were highly rated. The context-aware and spatial interaction have been recently emphasized as extended properties of tangible user interfaces. The major type of installation in the science museum is equipped with buttons and joysticks for physical manipulation, thus multimodal interfaces utilizing visual, aural, tactile senses etc need to be developed to provide more innovative interaction. Further, more installation need to be reconfigurable for embodied interaction between users and the interactive space. The proposed design guideline can specify the characteristics of tangible user interfaces, thus this research can be a basis for the development and application of installation involving more TUI properties in future.
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