https://scholar.dgist.ac.kr/handle/20.500.11750/155 Department of Robotics and Mechatronics Engineering, DGIST 2026-07-25T16:52:18Z https://scholar.dgist.ac.kr/handle/20.500.11750/60514 Title: Highly Conductive and Stretchable Photothermal CuSe Fiber for Wearable Electronics and Implantable Drug Release Systems Author(s): Yoon, Kukro; Lee, Yukye; Rhee, Sang Ki; Park, Hyeonjoo; Kang, Kyowon; Sang, Mingyu; Kim, Byeonggwan; Lee, Jung Seung; Lee, Jaehong; Lee, Taeyoon Abstract: Recently, fiber electronics have emerged as promising platforms for next-generation wearable and biomedical systems because of their flexibility, light weight, and softness. Among them, photothermal fibers are particularly attractive for therapeutic applications because of their remote and localized heat generation. However, most existing photothermal fibers rely on near-infrared (NIR)-I-responsive materials, which exhibit shallow tissue penetration and limited efficiency, thereby restricting their applicability in tissue implantation. In this study, a stretchable photothermal copper (II) selenide (CuSe) fiber was fabricated via a simple solution-based synthesis. Owing to the uniformly embedded CuSe nanoplates within the polyurethane matrix, the fabricated CuSe fiber exhibits high electrical conductivity (3.419 S/cm), excellent stretchability (100% tensile strain), and stable heating up to 81.7 degrees C under NIR-II irradiation. When integrated into textiles, the fiber can function as both a reliable strain sensor (gauge factor = 28.88) and a wearable heater (photothermal conversion efficiency = 40.1%). In addition, an implantable NIR-triggered drug release fiber was developed by coating the prepared CuSe fiber with a thermo-responsive hydrogel, achieving photothermal-triggered drug release and exhibiting therapeutic efficacy in mice with lipopolysaccharide-induced sepsis. Overall, the developed NIR-II-responsive CuSe photothermal fiber provides a versatile and clinically relevant platform for next-generation wearable and biomedical systems. 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60501 Title: Finger-based 3D human-swarm interaction interface: Design and human-subject evaluation Author(s): Heo, Jinuk; Kim, Hyunsu; Lee, Eunhak; Lee, Youngseon; Park, Hyunreal; Huh, Seokhaeng; Lee, Seongjun; Lee, Yongseok; Lee, Dongjun Abstract: Human-swarm interaction (HSI) in 3D environments faces critical challenges, including the high degrees of freedom (DOFs) of large swarms and limited operator spatial awareness. To address these issues, we introduce a novel finger-based HSI interface capable of managing 100 or more agents. The interface integrates three core interaction methods-Attraction, Repulsion, and Relaxed-mapping-along with auxiliary utilities and viewpoint controls, leveraging finger dexterity for expressive and responsive swarm manipulation. We conducted rigorous human-subject studies across three scenarios: pattern formation, collective exploration, and coordinated navigation. Results demonstrate that our interface significantly outperforms the baseline in performance and workload, primarily due to efficient, implicit viewpoint control. We also found strong evidence for scenario-dependent optimality, where the effectiveness of interaction methods varied by task demands. Scalability analysis revealed that performance in macro-management tasks remained constant regardless of swarm size, whereas micro-management tasks scaled linearly. Furthermore, while objective performance and perceived workload generally correlated, user preference sometimes diverged when performance gains were marginal, highlighting the importance of intuitiveness. This study provides empirical insights for designing adaptive, context-aware HSI systems for large-scale human-swarm collaboration. 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60471 Title: Precision enhancement of epidural force-sensing needle with machine learning Author(s): Cho, Gichan; Na, Jongyeol; Lee, Myung Ho; Kwon, Hyun-Jung; Song, Cheol Abstract: Epidural injection is used in pain intervention, requiring precise needle placement within the epidural space. Traditional techniques, such as loss of resistance and fluoroscopy-guided procedures, have limitations, including reliance on subjective assessment and radiation exposure. We proposed an optical force-sensing probe with an offset criterion of the needle tip-distal end to enhance the precision of puncture detection. The offset between the needle tip and the force-sensing probe is adjusted using a piezoelectric motor-based system with feedback position control. A Long Short-Term Memory model is also trained to detect the puncture. Insertion test on silicone phantom and ex-vivo specimens demonstrates that the system's offset range for enhancing precision of puncture detection is between 0.6 mm and 1 mm. Compared to the offset in the previous study, the AUC score of puncture detection increased from 0.61 to 0.86. This approach secures the improvement of puncture detection reliability in robot-assisted epidural injection. 2026-11-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60460 Title: EVA-SrBi2Nb2O9 composites for energy harvesting and AI-integrated finger strength monitoring Author(s): Mohanty, Raj; Kaja, Kushal Ruthvik; Hajra, Sugato; Behera, Swayam Aryam; Panigrahi, Basanta Kumar; Kim, Hoe Joon; Achary, P. Gang Raju Abstract: Triboelectric nanogenerators (TENG) offer an effective approach for converting ambient mechanical energy into electrical power. In this study, a TENG incorporating a composite film composed of Ethylene-vinyl acetate-SrBi2Nb2O9 (EVA-SBN) is designed and evaluated. The SBN material is synthesised via a solid-state method. When the EVA-SBN 5 wt% triboelectric layer is coupled with PDMS as the counter triboelectric material, the device delivers an output voltage of 154 V, a current of around 334 nA, and a maximum power of 102 mu W. Further, in this work, the demonstration of powering a calculator using TENG was performed. The response of finger impact upon the EVA-SBN/PDMS-based TENG was traced, and using artifical neural network (ANN) based prediction of individual finger strength enables accurate, real-time hand motion recognition for applications in smart rehabilitation, prosthetics, and human-machine interfaces. 2026-04-30T15:00:00Z