Design and development of fabric-type fitness band

Dawun Jeong,Sojung Lee,Chae-Ryung Kwon,Ihwa Park,Seowon Heo,Dong-Eun Kim 복식문화학회 2018 服飾文化硏究 Vol.26 No.4

This study aims to contribute to the development of sports wearables. It was conducted by a convergence team of professionals in the fashion industry, kinesiology and sports studies, and computer science and engineering. The purpose of the current study was to design and develop a fabric-type fitness band for a sensor to measure acceleration during jump rope exercises. Computer science and engineering professionals developed the Arduino board and sensor, kinesiology and sports studies provided the necessary exercise protocol, and the fashion industry professionals developed the band. First, a fitness band preference survey was completed by men and women between the ages of 20 and 50. Typical uses of the band included tracking exercise amount as measured by the number of steps taken and calories burned. Strap watch closure, a single color and achromatic color, and soft and smooth touch materials were preferred as band design. Second, two fabric-type fitness bands were designed and developed. Design 1 had a 3-dimensional pocket for the sensor, bright blue color, and stretch binding around the edges and for a loop. Design 2 had a flat pocket for the sensor, achromatic color, mesh binding around the edges and two metal loops. Both designs had Velcro as a closure. Third, wear testing of both bands with the sensor were conducted of 15 women in their 20s. They wore the bands during jump rope exercises. Both bands generally satisfied the participants. The Design 2 band was slightly more satisfying than the Design 1 band.

CT Radiomic Features의 재현성 향상을 위한 Super-Resolution GAN 기반의 CT 영상 변환 연구

정다운(Dawun Jeong),이지나(Jina Lee),홍영택(Youngtaek Hong),조연진(Yeon Jin Cho),심학준(Hackjoon Shim),장혁재(Hyuk-Jae Chang) 대한전자공학회 2020 대한전자공학회 학술대회 Vol.2020 No.8

Computed tomography (CT) is a widely used modality for diagnosing lesions, but the physician decides imaging protocols. Depending on the imaging protocol, different Radiomic features are varied, which is a significant obstacle to Radiomics research. Therefore, in this study, abdominal phantom images from various protocols were converted to reference protocol images using a generative adversarial network. The radiomic features by the proposed generative adversarial network showed improved correlation with reference protocol imaging in texture features. In this study, we showed the feasibility of CT image standardization.

Effect of Taping Therapy and Inner Arch Support on Plantar Lower Body Alignment and Gait

Lee, Sojung,Jeong, Dawun,Kim, Dong-Eun,Yi, Kyungock Korean Society of Sport Biomechanics 2017 한국운동역학회지 Vol.27 No.3

Objective: The purpose of this study was to identify the effects of taping therapy and inner arch support on pes planus lower extremity alignment and gait. Method: The study was conducted on 13 women in their 20s who had pes planus and no gait problems. Independent variables were the condition of wearing basic socks (S1) and the condition of wearing socks with taping therapy and inner arch support (S2). The dependent variables were resting calcaneal stance position (RCSP), plantar pressure distribution during gait, and underlying and medial longitudinal arch angle measured using radiography. Statistical analysis was performed using the Wilcoxon test with SPSS 23.0 for comparison of S1 and S2. Results: In the RCSP measurement, the angle range of S2 changed to normal. Meary's angle appeared to be less than the angle of S1, indicating alleviation of the degree of pes planus. The calcaneal pitch angle increased at S2 from that at S1. The plantar pressure distribution was divided into four areas (toe, forefoot, midfoot, and hindfoot). At S2, the maximum pressure increased in the toe and midfoot. The maximum force increased significantly in the toe and midfoot but decreased significantly in the forefoot and hindfoot. In addition, the contact area increased overall especially at the midfoot and hindfoot. Contact time decreased in the toe and forefoot, but increased in the midfoot and hindfoot. Conclusion: Taping therapy and inner arch support showed structural improvement of the pes planus. In addition, the force and pressure applied to the foot during walking are distributed evenly in the area of the sole, thus positively affecting walking.

Effect of Taping Therapy and Inner Arch Support on Plantar Lower Body Alignment and Gait

( Sojung Lee ),( Dawun Jeong ),( Dong-eun Kim ),( Kyungock Yi ) 한국운동역학회 2017 한국운동역학회지 Vol.27 No.3

Objective: The purpose of this study was to identify the effects of taping therapy and inner arch support on pes planus lower extremity alignment and gait. Method: The study was conducted on 13 women in their 20s who had pes planus and no gait problems. Independent variables were the condition of wearing basic socks (S1) and the condition of wearing socks with taping therapy and inner arch support (S2). The dependent variables were resting calcaneal stance position (RCSP), plantar pressure distribution during gait, and underlying and medial longitudinal arch angle measured using radiography. Statistical analysis was performed using the Wilcoxon test with SPSS 23.0 for comparison of S1 and S2. Results: In the RCSP measurement, the angle range of S2 changed to normal. Meary`s angle appeared to be less than the angle of S1, indicating alleviation of the degree of pes planus. The calcaneal pitch angle increased at S2 from that at S1. The plantar pressure distribution was divided into four areas (toe, forefoot, midfoot, and hindfoot). At S2, the maximum pressure increased in the toe and midfoot. The maximum force increased significantly in the toe and midfoot but decreased significantly in the forefoot and hindfoot. In addition, the contact area increased overall especially at the midfoot and hindfoot. Contact time decreased in the toe and forefoot, but increased in the midfoot and hindfoot. Conclusion: Taping therapy and inner arch support showed structural improvement of the pes planus. In addition, the force and pressure applied to the foot during walking are distributed evenly in the area of the sole, thus positively affecting walking.

직물형 피트니스 밴드 디자인 및 개발

정다운,이소정,권채령,박이화,허서원,김동은,Jeong, Dawun,Lee, Sojung,Kwon, Chae-Ryung,Park, Ihwa,Heo, Seowon,Kim, Dong-Eun 복식문화학회 2018 服飾文化硏究 Vol.26 No.4

This study aims to contribute to the development of sports wearables. It was conducted by a convergence team of professionals in the fashion industry, kinesiology and sports studies, and computer science and engineering. The purpose of the current study was to design and develop a fabric-type fitness band for a sensor to measure acceleration during jump rope exercises. Computer science and engineering professionals developed the Arduino board and sensor, kinesiology and sports studies provided the necessary exercise protocol, and the fashion industry professionals developed the band. First, a fitness band preference survey was completed by men and women between the ages of 20 and 50. Typical uses of the band included tracking exercise amount as measured by the number of steps taken and calories burned. Strap watch closure, a single color and achromatic color, and soft and smooth touch materials were preferred as band design. Second, two fabric-type fitness bands were designed and developed. Design 1 had a 3-dimensional pocket for the sensor, bright blue color, and stretch binding around the edges and for a loop. Design 2 had a flat pocket for the sensor, achromatic color, mesh binding around the edges and two metal loops. Both designs had Velcro as a closure. Third, wear testing of both bands with the sensor were conducted of 15 women in their 20s. They wore the bands during jump rope exercises. Both bands generally satisfied the participants. The Design 2 band was slightly more satisfying than the Design 1 band.

Deep Learning-Based Computed Tomography Image Standardization to Improve Generalizability of Deep Learning-Based Hepatic Segmentation

Seul Bi Lee,Youngtaek Hong,Yeon Jin Cho,Dawun Jeong,Jina Lee,Soon Ho Yoon,Seunghyun Lee,Young Hun Choi,Jung-Eun Cheon The Korean Society of Radiology 2023 Korean Journal of Radiology Vol.24 No.4

Objective: We aimed to investigate whether image standardization using deep learning-based computed tomography (CT) image conversion would improve the performance of deep learning-based automated hepatic segmentation across various reconstruction methods. Materials and Methods: We collected contrast-enhanced dual-energy CT of the abdomen that was obtained using various reconstruction methods, including filtered back projection, iterative reconstruction, optimum contrast, and monoenergetic images with 40, 60, and 80 keV. A deep learning based image conversion algorithm was developed to standardize the CT images using 142 CT examinations (128 for training and 14 for tuning). A separate set of 43 CT examinations from 42 patients (mean age, 10.1 years) was used as the test data. A commercial software program (MEDIP PRO v2.0.0.0, MEDICALIP Co. Ltd.) based on 2D U-NET was used to create liver segmentation masks with liver volume. The original 80 keV images were used as the ground truth. We used the paired t-test to compare the segmentation performance in the Dice similarity coefficient (DSC) and difference ratio of the liver volume relative to the ground truth volume before and after image standardization. The concordance correlation coefficient (CCC) was used to assess the agreement between the segmented liver volume and ground-truth volume. Results: The original CT images showed variable and poor segmentation performances. The standardized images achieved significantly higher DSCs for liver segmentation than the original images (DSC [original, 5.40%-91.27%] vs. [standardized, 93.16%-96.74%], all P < 0.001). The difference ratio of liver volume also decreased significantly after image conversion (original, 9.84%-91.37% vs. standardized, 1.99%-4.41%). In all protocols, CCCs improved after image conversion (original, -0.006-0.964 vs. standardized, 0.990-0.998). Conclusion: Deep learning-based CT image standardization can improve the performance of automated hepatic segmentation using CT images reconstructed using various methods. Deep learning-based CT image conversion may have the potential to improve the generalizability of the segmentation network.