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Author

Do Hyung Kim

Bio: Do Hyung Kim is an academic researcher from Korea University of Science and Technology. The author has contributed to research in topics: Paeonol & Modality (human–computer interaction). The author has co-authored 3 publications.

Papers
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Proceedings ArticleDOI
12 Jul 2021
TL;DR: Wang et al. as discussed by the authors proposed a new mid-level feature fusion method for two-stream based action recognition network, which leverages a whole feature map from each modality and achieves competitive performance in various experimental settings, especially for domain changing situations.
Abstract: This paper addresses a practical action recognition method for elderly-care robots. Multi-stream based models are one of the promising approaches for solving the complexity of real-world environments. While multi-modal action recognition have been actively studied, there is a lack of research on models that effectively combine features of different modalities. This paper proposes a new mid-level feature fusion method for two-stream based action recognition network. In multi-modal approaches, extracting complementary information between different modalities is an essential task. Our network model is designed to fuse features at an intermediate level of feature extraction, which leverages a whole feature map from each modality. Consensus feature map and consensus attention mechanism are proposed as effective ways to extract information from two different modalities: RGB data and motion features. We also introduce ETRI-Activity3D-LivingLab, a real-world RGB-D dataset for robots to recognize daily activities of the elderly. It is the first 3D action recognition dataset obtained in a variety of home environments where the elderly actually reside. We expect our new dataset to contribute to the practical study of action recognition with the previously released ETRI-Activity3D dataset. To prove the effectiveness of the method, extensive experiments are performed on NTU RGB+D, ETRI-Activity3D and, ETRI-Activity3D-LivingLab dataset. Our mid-level fusion method achieves competitive performance in various experimental settings, especially for domain-changing situations.

2 citations

Journal ArticleDOI
TL;DR: It is shown here how the cell senescence-like properties of the Tournaisian nervous system can affect the ability of the immune system to defend itself against infection.
Abstract: 세포 노쇠화(cell senescence)는 나이 듦에 따른 내인성 노화 및 질병들에서 나타날 수 있는 세포의 노화인자 발현, 세포분열 정지 등의 현상으로 일컬어진다. 피부세포의 경우, 노화 및 외부요인으로 인한 세포 노쇠화가 일어나 세포분열의 정지 및 기능 이상이 관찰되며 이는 피부노화를 가속화시키는 요인이 된다. 본 연구에서는, cordycepin을 이용하여 노화된 피부세포의 세포 노쇠화 억제 및 기능 향상을 유도하여 피부노화 개선의 가능성을 제시하였다. 사람에서 유래한 섬유아세포를 이용하여 세포의 ${\beta}$ -galactosidase 활성 세포염색 결과, 많은 계대의 세포에서 발현이 높게 나타남을 알 수 있었다. 항산화 및 항염 효과가 알려진 cordycepin을 많은 계대의 세포에 처리하였을 때 ${\beta}$ -galactosidase 활성이 확연히 떨어짐을 확인하였고 무혈청 배지 조건에서 많은 계대 세포의 증식 및 생존율을 높이는 결과를 보였으며 세포 노쇠화와 많은 연관성이 대두되고 있는 미토콘드리아의 기능관련 실험을 진행한 결과, 높은 ROS억제능이 나타났다. 본 연구를 통하여 노화된 사람 피부 섬유아세포에서의 cordycepin의 세포 노화 개선능을 알 수 있었으며, 피부 항노화소재로서의 가능성을 확인하였다. 【Cell senescence can be identified by cellular changes that occur as a result of intrinsic aging and/or diseases. In case of skin cells, aging and cell senescence caused by external factors results in cessation of cell proliferation and cellular malfunction, which, in turn, accelerates skin aging. In this study, inhibition of cell senescence and enhancement of cell function were studied using cordycepin to evaluate the potential for skin anti-aging agent. By comparing with the number of senescence associated with ${\beta}$ -galactosidase (SA- ${\beta}$ -gal) positive cells in young and replicative aged human fibroblasts, it was found that replicative aged cells showed higher expression of ${\beta}$ -galactosidase. Treatment of cordycepin - known as an anti-oxidative and anti-inflammatory agent - reduced ${\beta}$ -galactosidase expression in senescent cells and enhanced cell survival in serum-free culture condition. Cordycepin also showed superb inhibition of ROS, which is another indicator of cell senescence. The results of this study proved the anti-aging effect of cordycepin on human fibroblasts and also proposed a possibility of its use as an anti-aging cosmetic ingredient.】

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Proceedings ArticleDOI
29 May 2023
TL;DR: In this paper , a novel deep learning human activity recognition and classification architecture capable of autonomously identifying ADLs in home environments to enable long-term deployment of socially assistive robots to aid older adults.
Abstract: Many older adults prefer to stay in their own homes and age-in-place. However, physical and cognitive limitations in independently completing activities of daily living (ADLs) requires older adults to receive assistive support, often necessitating transitioning to care centers. In this paper, we present the development of a novel deep learning human activity recognition and classification architecture capable of autonomously identifying ADLs in home environments to enable long-term deployment of socially assistive robots to aid older adults. Our deep learning architecture is the first to use multimodal inputs to create an embedding vector approach for classifying and monitoring multiple ADLs. It uses spatial mid-fusion to combine geometric, motion and semantic features of users, environments, and objects to classify and track ADLs. We leverage transfer learning to extract generic features using the early layers of deep networks trained on large datasets to apply our architecture to various ADLs. The embedding vector enables identification of unseen ADLs and determines intra-class variance for monitoring user ADL performance. Our proposed unique architecture can be used by socially assistive robots to promote reablement in the home via autonomously supporting the assistance of varying ADLs. Extensive experiments show improved classification accuracy compared to unimodal/dual-modal models and the ADL embedding space also incorporates the ability to distinctly identify and track seen and unseen ADLs.