https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/666 Sat, 04 Apr 2026 08:36:26 GMT 2026-04-04T08:36:26Z https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78260 Title: Cardiac fibroblast-mediated ECM remodeling regulates maturation in an in vitro 3D engineered cardiac tissue Authors: Jang, Yongjun; Kang, Myeongjin; Kang, Yong Guk; Lee, Dongtak; Jung, Hyo Gi; Yoon, Dae Sung; Kim, Jongseong; Park, Yongdoo Abstract: Cardiac fibroblasts play an important role in heart homeostasis, regeneration, and disease by producing extracellular matrix (ECM) proteins and remodeling enzymes. Under normal conditions, fibroblasts exist in a quiescent state and maintain homeostasis, such as tissue structure and ECM turnover. However, if they become activated upon stimuli, such as injury, aging, or mechanical stress, which can lead to disease through excessive cell proliferation and ECM production. In addition to their role in disease progression, it remains unclear how cardiac fibroblasts contribute to cardiac maturation during development and whether the mechanism driving cytokine and ECM production during development aligns with those observed in pathological conditions. In this study, we investigated the functional and structural maturation of engineered cardiac tissue by modulating fibroblast activity within a three-dimensional (3D) in vitro model. In this model, human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) and human primary cardiac fibroblasts (FBs) were co-cultured in a fibrin gel and their morphology, beating characteristics, beating force, and mRNA expression profiles were analyzed. The results demonstrate that functional and structural maturation were enhanced by fibroblast-driven tissue contraction and collagen deposition, while inhibition of ECM remodeling impaired both processes. However, excessive collagen accumulation reduced functional maturation by limiting contractile efficiency. Our data suggest that ECM remodeling by cardiac fibroblasts is essential for cardiac tissue maintenance and maturation. Additionally, the regulation of collagen deposition by fibroblast activity will be a key focus of future research, as it may critically influence both cardiac development and the progression of heart disease. Fri, 01 Aug 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/78260 2025-08-01T00:00:00Z 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/77539 Title: Deep Learning-Based Recognition of Locomotion Mode, Phase, and Phase Progression Using Inertial Measurement Units Authors: Kim, Ye Kwang; Kim, Jae wook; Moon, Ju Hui; Kang, Seong Hyun; Shim, Young Bo; Choi, Mun Taek; Kim, Seung Jong Abstract: Recently, wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities, which emphasize modularization, simplification, and weight reduction. Thus, synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability, which requires accurate recognition of the user’s gait intent. In this study, we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression. Utilizing data from five inertial measurement units placed on the body, the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases. Subsequently, phase progression is estimated through 1D convolutional neural network-based regressors, each dedicated to a specific phase. The model was evaluated on a diverse dataset encompassing level walking, stair ascent and descent, and sit-to-stand activities from 10 healthy participants. The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression. Accurate phase progression estimation is essential due to the age-related variability in gait phase durations, particularly evident in older adults, the primary demographic for gait-assist robots. These findings underscore the potential to enhance the assistance, comfort, and safety provided by gait-assist robots. Thu, 01 May 2025 00:00:00 GMT https://scholarworks.korea.ac.kr/kumedicine/handle/2021.sw.kumedicine/77539 2025-05-01T00:00:00Z