研究業績

@article{9429258,
title = {Exploring the Impacts of Elaborateness and Indirectness in a Behavior Change Support System},
author = {Zhihua Zhang and Juliana Miehle and Yuki Matsuda and Manato Fujimoto and Yutaka Arakawa and Keiichi Yasumoto and Wolfgang Minker},
doi = {10.1109/ACCESS.2021.3079473},
issn = {2169-3536},
year = {2021},
date = {2021-05-12},
journal = {IEEE Access},
volume = {9},
pages = {74778-74788},
abstract = {Numerous technologies exist for promoting a healthier lifestyle. These technologies collectively referred to as “Behavior Change Support Systems”. However, the majority of existing apps use quantitative data representation. Since it is difficult to understand the meaning behind quantitative data, this approach has been suggested to lower users’ motivation and fail to promote behavior change. Therefore, an interpretation of quantitative data needs to be provided as a supplement. However, different descriptions of the same data may lead to different outcomes. In this paper, we explore the impact of different communication styles for interpretations of quantitative data on behavior change by developing and evaluating Walkeeper \textendash a web-based app that provides interpretations of the users’ daily step counts using different levels of elaborateness and indirectness with the aim of promoting walking. Through the quantitative analysis and results of a user study, we contribute new knowledge on designing such interpretations for quantitative data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{9387296,
title = {Detection of Careless Responses in Online Surveys Using Answering Behavior on Smartphone},
author = {Masaki Gogami and Yuki Matsuda and Yutaka Arakawa and Keiichi Yasumoto},
doi = {10.1109/ACCESS.2021.3069049},
issn = {2169-3536},
year = {2021},
date = {2021-03-26},
journal = {IEEE Access},
volume = {9},
pages = {53205 - 53218},
abstract = {Some respondents make careless responses due to the “satisficing,” which is an attempt to complete a questionnaire as quickly and easily as possible. To obtain results that reflect a fact, detecting satisficing and excluding the responses with satisficing from the analysis targets are required. One of the devised methods detects satisficing by adding questions that check violations of instructions and inconsistencies. However, this approach may cause respondents to lose their motivation and prompt them to satisficing. Additionally, a deep learning model that automatically answers these questions was reported. This threatens the reliability of the conventional method. To detect careless responses without inserting such screening questions, machine learning (ML) detection using data obtained from answer results was attempted in a previous study, with a detection rate of 55.6%, which is not sufficient from the viewpoint of practicality. Therefore, we hypothesized that a supervised ML model with a higher detection rate could be constructed by using on-screen answering behavior as features. However, (1) no existing questionnaire system can record on-screen answering behavior and (2) even if the answering behavior can be recorded, it is unclear which answering behavior features are associated with satisficing. We developed an answering behavior recording plug-in for LimeSurvey, an online questionnaire system used all over the world, and collected a large amount of data (from 5,692 people) in Japan. Then, a variety of features were examined and generated from answering behavior, and we constructed ML models to detect careless responses.We call this detection method the ML-ABS (ML-based answering behavior scale). Evaluation by cross-validation demonstrated that the detection rate for careless responses was 85.9%, which is much higher than the previous ML method. Among the various features we proposed, we found that reselecting the Likert scale and scrolling particularly contributed to the detection of careless responses.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{9345686,
title = {Measuring “Nigiwai” from Pedestrian Movement},
author = {Mohamed A. Abdelwahab, Shizuo Kaji, Maiya Hori, Shigeru Takano, Yutaka Arakawa, Rin-ichiro Taniguchi},
url = {https://youtu.be/Jd_vKuU4HXI},
doi = {10.1109/ACCESS.2021.3056698},
issn = {2169-3536},
year = {2021},
date = {2021-02-05},
journal = {IEEE Access},
volume = {9},
pages = {24859 - 24871},
abstract = {The analysis of the movement of people in a shopping area with the aim of improving marketing is an important research topic. Many conventional methods are dependent on the density of people in the area, which is easily estimated by counting the people entering or exiting the area. However, a high density does not always mean an increase in activity, as certain people are simply passing the area at a given time. The primary goal of this study was to introduce a set of indicators for measuring the bustle of the area, which we call “Nigiwai,” from pedestrian movement by using an analogy from classical kinematics. Such indicators can be used to measure the impact of promotional events and to optimize the design of the area. Our novel indicators were evaluated with simulated pedestrian scenarios and were demonstrated to distinguish shopping scenarios from those in which people move around without shopping successfully, even when the latter scenarios had much higher densities. The indicators were computed solely from the pedestrian trajectory, which can easily be obtained from ordinary sensors using deep learning-based techniques. As a demonstration with real data, we applied our method to a video of a street and provided a visualization of the indicators.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}