https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/668 Sat, 04 Apr 2026 08:36:27 GMT 2026-04-04T08:36:27Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78422 Title: Simultaneous Recognition of Locomotion Mode, Phase, and Phase Progression Using Deep Learning Models Authors: Kim, Yekwang; Kim, Jaewook; Moon, Juhui; Choi, Mun-Taek; Kim, Seung-Jong Abstract: Despite advances in gait-assist wearable robots, application in real-world scenarios remains limited, largely due to challenges in developing an effective user intention recognition algorithm. These algorithms are crucial as they enable the robot to move harmoniously with the user by predicting their intent during various locomotion activities such as level walking, stair ascent, stair descent, and sit-to-stand. It is essential to not only identify these locomotion modes but also their phases and progression for real-time assistance. Traditional classification methods often require extensive manual feature extraction from signals like those from inertial measurement units (IMU), electromyography, and plantar force sensors. Recent machine learning, particularly deep learning approaches, have simplified this process through automatic feature extraction. However, no existing method simultaneously predicts locomotion modes, phases, and phase progression, which is significant for personalized assistance. This study introduces a deep learning framework that classifies locomotion modes and phases and estimates the phase progressions using IMU data from sensors placed on the sternum and limbs. Results from five participants show that our model effectively classifies the locomotion phase and well estimates the phase progression percentage. The model was evaluated using a leave-one-subject-out approach, ensuring generalizability across different users. Tue, 01 Jul 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78422 2025-07-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78423 Title: Biomechanical Effects of Modulating Pedaling Configurations on a Tilted-Plane Ergometer Authors: Kang, Seonghyun; Kim, Jaewook; Kim, Yekwang; Moon, Juhui; Kim, Seung-Jong Abstract: Excessive coronal or axial plane moments in lower extremity joints are commonly linked to musculoskeletal injuries. However, deliberately inducing such moments can be advantageous in personalized rehabilitation by addressing specific biomechanical needs. The tilted-plane ergometer, designed to tilt the pedaling plane and adjust pedal orientation, facilitates desired limb alignment during pedaling. This study examined changes in joint dynamics, and muscle activity across various ergometer configurations using a musculoskeletal modeling approach. Five participants performed pedaling tasks under four distinct configurations, with data collected via a motion capture system and a force-torque sensor integrated into the pedal. Configurations favoring abducted pedaling planes reduced knee adduction moments and increased ankle abduction moments, while axial rotation moments were modulated through targeted pedal orientations. Conversely, adducted pedaling planes combined with inverted pedal orientations significantly increased internal rotation moments at the knee and ankle, alongside inducing posterior directional forces at the hip and ankle joints. Estimated muscle activity varied across configurations, demonstrating the device's adaptability in targeting specific biomechanics. These findings underscore the potential of the tilted-plane ergometer to effectively modulate joint biomechanics, positioning it as a promising tool for personalized rehabilitation interventions. Tue, 01 Jul 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78423 2025-07-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/62846 Title: Evaluation of the efficacy of SDF-1-based novel polypeptides by structure-based drug design in an acute myocardial infarction model Authors: Lee, Kang-Gon; Santos, Ana Rita M. P.; Rang, Yong Guk; Chae, Yun Jin; Song, Myeongjin Myeongjin; Choi, Sangdun; Kim, Jongseong; Park, Yongdoo Abstract: [No abstract available] Thu, 01 Jan 20221001 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/62846 20221001-01-01T00:00:00Z https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/43656 Title: A CMOS impedance cytometer for 3D flowing single-cell real-time analysis with ΔΣ error correction Authors: Lee K.-H.; Nam J.; Choi S.; Lim H.; Shin S.; Cho G.-H. Abstract: Flow cytometry is an essential cell analysis technology in clinical immunology and haematology for the diagnosis and prognosis of disease. It involves the counting, identification and sorting of cells [1,2]. Conventional bulk measurements [3] require a large volume of blood, which is not desirable for the early detection of a disease, when only a very small percentage of cells contain evidence of the disease. In this paper, we propose, for the first time, a non-invasive and high-throughput single-cell analysis method using CMOS-integrated circuits in conjunction with a microfluidic channel as the first building block of a complete cell-sorting device. © 2012 IEEE. Tue, 01 Jan 201202 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/43656 201202-01-01T00:00:00Z