Sungjin Hwang

Transportation mode detection (TMD) for wheelchair users is essential for applications that facilitate enhancing accessibility and quality of life. Yet, the lack of extensive datasets from disabled individuals hinders the development of tailored TMD systems. Our study assesses two data collection methods in TMD for disability research: using non-wheelchair users to simulate wheelchair activities (Simulation Real IMU) and generating synthetic sensor data from videos (Virtual IMU). Results show that, when using a larger dataset and multiple sensor modalities, models trained on Simulation Real IMU perform better. However, models trained on both Simulation Real IMU and Virtual IMU exhibited similar performances when sensors were restricted to accelerometer and gyroscope only. This finding guides future researchers toward the use of Simulation Real IMU for comprehensive, multimodal sensor studies, provided they have sufficient budget and time. However, the more cost and time-efficient Virtual IMU can be a viable alternative in scenarios using basic sensors.

사단법인 한국컴퓨터그래픽스학회의 논문지로서 그래픽스 분야와 그에 연관된 연구영역의 새로운 발견과 최첨단 연구결과를 발표하는 토론의 장입니다. 학회를 대표하는 학술지인 논문지는 1995년에 논문지 발간을 시작으로 2000년부터 3월, 6월, 9월, 12월 총 4회 발간되었고, 2015년부터는 KCGS 학술대회 특별호를 7월에 발간하면서 총 5회 발간되고 있습니다.

Transportation mode detection (TMD) has been proposed as a computational technology to obtain mobility information. However, previous TMD studies mainly focused on improving detection performance and have not investigated the social implications of mobility information. This is the first study to use TMD to predict the life satisfaction of wheelchair users. Our goal is to develop TMD for wheelchair users (wTMD) utilizing smartphone location data and apply it to determine how transportation behaviors affect the life satisfaction of wheelchair users. First, we propose a wTMD technology by collecting a new dataset from wheelchair and non-wheelchair users. Second, we conduct regression analyses on existing in-the-wild dataset of wheelchair users. The result shows that the portion of subways in an individual's travel time is directly connected to wheelchair users' life satisfaction in Seoul, South Korea. We hope our findings are a good example for future social science studies and ultimately help to design wheelchair-friendly urban planning and accessibility.

Transportation mode detection (TMD) is a context-aware computing technology with significant potential in several applications. However, the development of TMD technologies for real-world scenarios remains challenging, including user-independent evaluations and multimodal analyses. In this study, our team (HYU-CSE) suggested a TMD model as part of the Sussex-Huawei Locomotion (SHL) recognition challenge, and we used the SHL motion and location data. The proposed TMD model was based on the DenseNet architecture, and post-processing using voting schemes was applied to refine the detection performance. The results suggested that the proposed method achieved 94.13% of an F1 score with user-independent analysis. We hope that our study will ultimately help in the design of better TMD applications.

Transportation mode detection (TMD) is an important research area in human activity recognition. It can improve the mobility and accessibility of people by providing a better understanding of their mobility patterns, thereby enhancing their quality of life and social inclusion. Although previous studies of TMD for people without mobility disabilities exhibited, the performance of TMD models on new users and periods was limited. This issue would be more important for people with mobility disabilities. This study investigated the negative impact of user and period differences on the performance of TMD for wheelchair users (wTMD) and suggests a method to address these challenges. Our main findings are (1) user and period differences degraded the wTMD performance from 94.28% to 59.32%; (2) the multi-DenseNet with a soft voting ensemble provided an 76.49% accuracy to data from different users and period. We expect that our understanding of wTMD will aid in the design of more generalized wTMD models.