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Investigation of Touch Screen based User Interface in a Driving Context
Seul Chan Lee(이슬찬) 대한인간공학회 2021 대한인간공학회 학술대회논문집 Vol.2021 No.11
Objective: This study aims to investigate the effects of in-vehicle information systems (IVIS)’ tasks based on visual-manual interaction and provide design guidelines for touchscreen-based IVIS. Background: The understanding of driver behavior when interacting with touchscreen-based IVIS is very low although the functions of IVIS become complex. Method: A driving simulation study was conducted to achieve the research objective. In the experiment, the participants were asked to perform item searching tasks, hierarchical menu tasks, and touch gesture tasks to observe driving safety-related behaviors, including driving performance, visual distraction, secondary task performance, and subjective workload. Statistical analyses and modeling techniques were applied to analyze the data. Results: The results showed that driving safety-related behaviors were influenced depending on design variables of the touchscreen of IVIS. Conclusion: This research provides insights for both researchers and practitioners on the design of IVIS in terms of driving safety.
자율주행 자동차 eHMI의 설계 공간에 대한 체계적 문헌 연구
이슬찬(Seul Chan Lee) 대한인간공학회 2020 大韓人間工學會誌 Vol.39 No.6
Objective: We aimed at a better understanding and analyzing the design space of external human-machine interfaces (eHMIs). Background: Autonomous vehicles are expected to become major components in future transportation system. The eHMIs can be widely utilized to interact with road users instead of drivers. Despite of their potential applicability, a holistic understanding of research method and design features for eHMIs remains limited. Method: We applied a systematic review method to achieve the research objective. A total of 26 articles were finally included for the review process. The selected articles were analyzed depending on the research questions we elicited. Results: As a result, we provided the results on design space including design variables and levels. Conclusion: According to our work, it reveals that different considerations should be integrated for developing and testing eHMIs. Application: Our work provides insights into the development and design of eHMIs, particularly for both practitioners and researchers to conceptualize, implement, and develop their own eHMIs.
이슬찬(Seul Chan Lee),윤솔희(Sol Hee Yoon),김민종(Meen Jong Kim),지용구(Yong Gu Ji) 한국HCI학회 2016 한국HCI학회 학술대회 Vol.2016 No.1
IT 기술의 발달과 적용으로 인해 차량 인터페이스는 점차 복잡해지는 추세에 있다. 하지만 인터페이스를 평가하기 위한 기존의 연구들은 주로 운전자의 주관적 의견에 기인한 평가를 실시하고 있다. 주관적 평가방법론은 편리하고 결과의 측정이 쉽다는 장점이 있지만, 이에 대한 원인을 탐색하기 어렵다는 한계가 있다. 따라서 본 연구는 복잡도의 개념을 활용하여 차량내 조작 인터페이스를 평가하기 위한 이론적 프레임워크를 제안하는데 목적이 있다. 이를 위해, human-computer interaction 관점 및 human factors 관점의 문헌연구를 통해 주요한 관점 및 측정 변수를 선정하였다. 평가 모형은 complexity dimension 과 interface evaluation dimension 으로 구축되어 있으며, 각각의 관점에서 측정변수를 도출하였다. 측정변수는 Functional factor 에서 5 개, behavioral factor 에서 6 개, structural factor 에서 13 개를 도출하였다. Advancement of technology is introducing new and more complex in-vehicle interfaces and system. However, previous researches focused on the evaluation of in-vehicle interfaces by mean of subjective general evaluation which makes it difficult to encounter the underlying problems of the interface when interacting with it. Therefore, the aim of this research is to develop an evaluation framework that enable to measure and evaluate the complexity of in-vehicle interface when interacting with it. The research is conducted based on Human-computer interaction and human factors theories. The evaluation framework takes into consideration two dimensions: complexity dimension (structural and relational) and interface evaluation dimension (functional, behavioral, and structural). Based on those dimensions, measurement variables were selected, presenting 5 measurement variables for functional factor, 6 for behavioral factors, and 13 for structural factors.
Suman Kalyan Sardar(수만 칼얀 사르다르),Naveen Kumar(나빈 쿠마르),Seul Chan Lee(이슬찬) 대한인간공학회 2021 대한인간공학회 학술대회논문집 Vol.2021 No.11
Objective: The purpose of this study is to evaluate the importance of major research areas by identifying different machine learning techniques that influence key research fields on Human Status Detection (HSD). Background: HSD is concerned with the study of human-system interactions that uses theory, concepts, data, and techniques to design in order to improve human well-being and total performance. The basic premise of HSD is that effective performance comes from user-centered design and a comprehensive understanding of the user’s skills, needs, and preferences. Several machine learning algorithms have been used in the literature to measure the cognitive and physical workload status of the users. Method: In this research, PRISMA model has been applied to gather articles from three databases namely, ScienceDirect, IEEE Xplore, and Association for Computing Machinery (ACM). Sixteen keywords has been selected for collecting the articles from the databases. The following criterion is considered to develop protocol: (1) inclusion/exclusion criteria, (2) study selection, (3) data extraction, (4) data synthesis. Results: A total number of 82 articles were identified using an iterative collaboration of 80 keyword combinations addressing issues in different physical workloads and cognitive loads. A list of important occurrences was identified that may have an impact on the publication pattern. Conclusion: Recent publications on human status detection appear to be primarily concerned with cognitive load whereas previous articles were on detecting physical workload. Application: This study helps domain researchers to identify HSD techniques for their experimental studies.