APPLICATION OF INFORMATION SYSTEMS IN TOWN COUNCILS

Bachelor'sBACHELOR OF SCIENCE (REAL ESTATE

Kansei clustering for emotional design using a combined design structure matrix

Consumers' emotional requirements, or so-called Kansei needs, have become one of the most important concerns in designing a product. Conventionally, Kansei engineering has been widely used to co-relate these requirements with product parameters. However, a typical Kansei engineering approach relies heavily on the intuition of the person who uses the method in clustering the Kansei adjectives, who may be the engineer or designer. As a result, the selection of Kansei adjectives may not be consistent with the consumers' opinions. In order to obtain a consumer-consistent result, all of the collected Kansei adjectives (usually hundreds) need to be evaluated by every survey participant, which is impractical in most design cases. Therefore, a Kansei clustering method based on a design structure matrix (DSM) is proposed in this work. The method breaks the Kansei adjectives up into a number of subsets so that each participant deals with only a portion of the words collected. Pearson correlations are used to establish the distances among the Kansei adjectives. The subsets are then integrated by merging the identical correlation pairs for an overall Kansei clustering result. The details of the proposed approach are presented and illustrated using a case study on wireless battery drills. The case study reveals that the proposed method is promising in handling Kansei adjective clustering problems

Products classification in emotional design using a basic-emotion based semantic differential method

Consumer’s emotional requirements, or the so-called Kansei needs, have become one of the most important concerns in product design nowadays. In this regard, the semantic differential (SD) method has been widely used in emotional product design and Kansei engineering to address the relationships between emotions and products. However, the conventional SD method assumes that the survey participants’ understandings on Kansei adjectives or tags are consistent, which might not be true for all design cases. As a result, classification of products using Kansei tags may not reflect a consumer’s genuine opinions. Accordingly, a basic-emotion based semantic differential method is proposed in this work. The proposed method improves the conventional SD method by taking variances of Kansei tags into consideration for better products classification in emotional design. It incorporates basic-emotion systems to identify Kansei variance and mapping functions in determining transformed values on Kansei-tag dimensions. Therefore, the adjusted Kansei mean values, which help classify products using Kansei tags, are obtained. The proposed approach is presented and illustrated using a case study of perfume bottle design. The results reveal that the proposed method is promising for handling product classifications in emotional design. Relevance to industry This study presents a generic method to establish the relationships between consumers’ Kansei needs and products for new product development. The knowledge gained from the method is beneficial in assisting the mapping of product domain into Kansei domain when applying Kansei engineering. Especially it helps to suggest a quantified range of each Kansei tag for product designers so that the links between products and Kansei requirements can be more clarified to them. It appears that the proposed method can be utilized to better classify products under Kansei tags as well as to facilitate decision-making in practical industrial design cases

Hierarchical eye-tracking data analytics for human fatigue detection at a traffic control center

Eye-tracking-based human fatigue detection at traffic control centers suffers from an unavoidable problem of low-quality eye-tracking data caused by noisy and missing gaze points. In this article, the authors conducted pioneering work by investigating the effects of data quality on eye-tracking-based fatigue indicators and by proposing a hierarchical-based interpolation approach to extract the eye-tracking-based fatigue indicators from low-quality eye-tracking data. This approach adaptively classified the missing gaze points and hierarchically interpolated them based on the temporal-spatial characteristics of the gaze points. In addition, the definitions of applicable fixations and saccades for human fatigue detection is proposed. Two experiments are conducted to verify the effectiveness and efficiency of the method in extracting eye-tracking-based fatigue indicators and detecting human fatigue. The results indicate that most eye-tracking parameters are significantly affected by the quality of the eye-tracking data. In addition, the proposed approach can achieve much better performance than the classic velocity threshold identification algorithm (I-VT) and a state-of-the-art method (U'n'Eye) in parsing low-quality eye-tracking data. Specifically, the proposed method attained relatively stable eye-tracking-based fatigue indicators and reported the highest accuracy in human fatigue detection. These results are expected to facilitate the application of eye movement-based human fatigue detection in practice.Accepted versio