Woohyeok Choi

Kangwon National University

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Uichin Lee

Korea Advanced Institute of Science and Technology

Auk Kim

Kangwon National University

Leontios J. Hadjileontiadis

Aristotle University of Thessaloniki

Yong Jeong

Korea Advanced Institute of Science and Technology

Heepyung Kim

Korea Advanced Institute of Science and Technology

Jeungmin Oh

Korea Advanced Institute of Science and Technology

Ahsan H. Khandoker

Khalifa University of Science and Technology

Cheul Young Park

Korea Advanced Institute of Science and Technology

Narae Cha

Korea Advanced Institute of Science and Technology

Soowon Kang

Korea Advanced Institute of Science and Technology

Cooperative Institutions

Korea Advanced Institute of Science and Technology

Kootenay Association for Science & Technology

University of Ulsan

Ulsan College

Khalifa University of Science and Technology

Asan Medical Center

Seoul National University

Aristotle University of Thessaloniki

Microsoft Research (United Kingdom)

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Effects of In-vehicle Auditory Interactions on Takeover Performance in SAE L2 Semi-automated Vehicles

International Journal of Human-Computer Studies (2024)

J.S. Hwang Woohyeok Choi Auk Kim

10.1016/j.ijhcs.2024.103401

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SwimTrain

Woohyeok Choi Jeungmin Oh Darren Edge Joohyun Kim Uichin Lee

We explore design opportunities for using interactive technologies to enrich group fitness exercises, such as group spinning and swimming, in which an instructor guides a workout program and members synchronously perform a shared physical activity. As a case study, we investigate group fitness swimming. The design challenge is to coordinate a large group of people by considering trade-offs between social awareness and information overload. Our resulting group fitness swimming game, SwimTrain, allows a group of people to have localized synchronous interactions over a virtual space. The game uses competitive and cooperative phases to help group members acquire group-wide awareness. The results of our user study showed that SwimTrain provides socially-enriched swimming experiences, motivates swimmers to follow a training regimen and exert more intensely, and allows strategic game play dealing with skill differences among swimmers. Consequently, we propose several practical considerations for designing group fitness exergames.

10.1145/2858036.2858579

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Interrupting Drivers for Interactions

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies (2018)

Auk Kim Woohyeok Choi Jung-Mi Park Kyeyoon Kim Uichin Lee

Auditory-verbal interactions with in-vehicle information systems have become increasingly popular for improving driver safety because they obviate the need for distractive visual-manual operations. This opens up new possibilities for enabling proactive auditory-verbal services where intelligent agents proactively provide contextualized recommendations and interactive decision-making. However, prior studies have warned that such interactions may consume considerable attentional resources, thus negatively affecting driving performance. This work aims to develop a machine learning model that can find opportune moments for the driver to engage in proactive auditory-verbal tasks by using the vehicle and environment sensor data. Given that there is a lack of definition about what constitutes interruptibility for auditory-verbal tasks, we first define interruptible moments by considering multiple dimensions and then iteratively develop the experimental framework through an extensive literature review and four pilot studies. We integrate our framework into OsmAnd, an open-source navigation service, and perform a real-road field study with 29 drivers to collect sensor data and user responses. Our machine learning analysis shows that opportune moments for interruption can be conservatively inferred with an accuracy of 0.74. We discuss how our experimental framework and machine learning models can be used to design intelligent auditory-verbal services in practical deployment contexts.

10.1145/3287053

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MobyDick

Woohyeok Choi Jeungmin Oh Taiwoo Park Seongjun Kang Miri Moon

The unique aquatic nature of swimming makes it very difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose MobyDick, a smartphone-based multi-player exergame designed to be used while swimming, in which a team of swimmers collaborate to hunt down a virtual monster. In this paper, we present a novel, holistic game design that takes into account both human factors and technical challenges. Firstly, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Secondly, we develop a single phone-based inertial and barometric stroke activity recognition system to enable precise, real-time game inputs. Thirdly, we carefully devise a multi-player interaction mode viable in the underwater environment highly limiting the abilities of human communication. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and deploy it to real swimming pool environments (n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multi-player swimming games.

Limiting

10.1145/2668332.2668352

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Exploring user experiences of active workstations

Woohyeok Choi Aejin Song Darren Edge Masaaki Fukumoto Uichin Lee

Prolonged inactivity in office workers is a well-known contributor to various diseases, such as obesity, diabetes, and cardiovascular dysfunction. In recent years, active workstations that incorporate physical activities such as walking and cycling into the workplace have gained significant popularity, owing to the accessibility of the workouts they offer. While their efficacy is well documented in medical and physiological literature, research regarding the user experience of such systems has rarely been performed, despite its importance for interactive systems design. As a case study, we focus on active workstations that incorporate under desk elliptical trainers, and conduct controlled experiments regarding work performance and a four week long field deployment to explore user experience with 13 participants. We investigate how such workouts influence work performance, when and why workers work out during working hours, and the general feelings of workers regarding usage. Our experimental results indicate that while work performance is not influenced, the cognitive load of tasks critically influences workout decisions. Active workstations were alternatively used as mood enhancers, footrests, and for fidgeting, and there exist unique social and technical aspects to be addressed, such as noise issues and space constraints. Our results provide significant implications for the design of active workstations and interactive workplaces in general.

Workstation

Desk

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10.1145/2971648.2971756

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White matter tract alterations in schizophrenia identified by DTI-based probabilistic tractography: a multisite harmonisation study

Acta Neuropsychiatrica (2024)

Young Tak Jo Sung Woo Joo Woohyeok Choi Soohyun Joe Jung Sun Lee

Abstract Introduction: It has been suggested that schizophrenia involves dysconnectivity between functional brain regions and also the white matter structural disorganisation. Thus, diffusion tensor imaging (DTI) has widely been used for studying schizophrenia. However, most previous studies have used the region of interest (ROI) based approach. We, therefore, performed the probabilistic tractography method in this study to reveal the alterations of white matter tracts in the schizophrenia brain. Methods: A total of four different datasets consisted of 189 patients with schizophrenia and 213 healthy controls were investigated. We performed retrospective harmonisation of raw diffusion MRI data by dMRIharmonisation and used the FMRIB Software Library (FSL) for probabilistic tractography. The connectivities between different ROIs were then compared between patients and controls. Furthermore, we evaluated the relationship between the connection probabilities and the symptoms and cognitive measures in patients with schizophrenia. Results: After applying Bonferroni correction for multiple comparisons, 11 different tracts showed significant differences between patients with schizophrenia and healthy controls. Many of these tracts were associated with the basal ganglia or cortico-striatal structures, which aligns with the current literature highlighting striatal dysfunction. Moreover, we found that these tracts demonstrated statistically significant relationships with few cognitive measures related to language, executive function, or processing speed. Conclusion: We performed probabilistic tractography using a large, harmonised dataset of diffusion MRI data, which enhanced the statistical power of our study. It is important to note that most of the tracts identified in this study, particularly callosal and cortico-striatal streamlines, have been previously implicated in schizophrenia within the current literature. Further research with harmonised data focusing specifically on these brain regions could be recommended.

10.1017/neu.2024.2

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Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia

Schizophrenia (2024)

Sung Woo Joo Young Tak Jo Woohyeok Choi Sun Min Kim So Young Yoo

Abstract A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman’s rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.

Similarity (geometry)

10.1038/s41537-024-00477-x

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Citations (1)

Designing Interactive Multiswimmer Exergames

ACM Transactions on Sensor Networks (2016)

Woohyeok Choi Jeungmin Oh Taiwoo Park Seongjun Kang Miri Moon

The unique aquatic nature of swimming makes it difficult to use social or technical strategies to mitigate the tediousness of monotonous exercises. In this study, we propose the use of a smartphone-based multiplayer exergame named MobyDick . MobyDick is designed to be played while swimming, where a team of swimmers collaborate to hunt down a virtual monster. To this end, we take into account both human factors and technical challenges under swimming contexts. First, we perform a comparative analysis of a variety of wireless networking technologies in the aquatic environment and identify various technical constraints on wireless networking. Second, we develop a swimming activity recognition system to enable precise and real-time game inputs. Third, we devise a multiplayer game design by employing the unique interaction mode viable in an underwater environment, where the abilities of human communication are highly limited. Finally, we prototype MobyDick on waterproof off-the-shelf Android phones, and we deploy it in real swimming pool environments ( n = 8). Our qualitative analysis of user interview data reveals certain unique aspects of multiplayer swimming games.

10.1145/2888399

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Citations (10)

A Design of Application through Physical Therapy Big Data Analytics

Journal of Multimedia Information System (2018)

Woohyeok Choi Jun‐Ho Huh

Data Analysis

10.9717/jmis.2018.5.3.171

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K-EmoPhone, A Mobile and Wearable Dataset with In-Situ Emotion, Stress, and Attention Labels

Zenodo (CERN European Organization for Nuclear Research) (2022)

Soowon Kang Woohyeok Choi Cheul Young Park Narae Cha Auk Kim

ABSTRACT: With the popularization of low-cost mobile and wearable sensors, many prior studies used such sensors to track and analyze people's mental well-being, productivity, and behavioral patterns. However, there is a lack of open datasets collected in real-world contexts with affective and cognitive state labels such as emotion, stress, and attention. This limits the advances in affective computing and human-computer interaction research. In this work, we present K-EmoPhone, an in-the-wild naturalistic dataset (n=80, 1-week) of smartphone use, wearable sensing, and self-reported affect states from college students. The dataset contains continuous probing of peripheral physiological signals and mobility data measured by off-the-shelf commercial devices in addition to context and interaction data by users' smartphones. Moreover, the dataset includes self-reports of in-situ affect states (n=5,753) such as emotion, stress level, attention level, and disturbance level, acquired by the experience sampling method. The resulting K-EmoPhone dataset helps to advance the research and development of affective computing, emotion intelligence technologies, and attention management based on mobile and wearable sensor data. Last update: Aug. 3, 2022 ----------------------------- * Version 1.0.0 (Aug. 3, 2022) Added P##.zip files, where each P## means the separate participant. Added SubjData.zip file, which includes individual characteristics information and labels.

10.5281/zenodo.6851298

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