관절 데이터 기반 동작 인식 모델 연합학습 프레임워크 연구
- Resource Type
- Article
- Authors
- 방준일; 홍성은; 전석환; 이주원; 김화종
- Source
- 한국정보기술학회논문지 (2023): 39-48.
- Subject
- Language
- Korean
- ISSN
- 15988619
This study corresponds to the implementation of federated learning among the systems that help caregivers taking care of many patients in a nursing hospital by photographing a nursing hospital patient with a bedside imaging device and building a motion recognition model with this image. De-identified and lightweight ETRI-Activity3D joint data was used for federated learning of the graph-based motion recognition deep learning model, and lightweight STGCN(Spatio-Temporal Graph Convolutional Networks) based motion recognition model was used for federated learning of time-series graphs. model was modified. Federated learning was implemented based on the open source Flower. The global model collected by the aggregation algorithm in the federated learning client showed better Accuracy than the model using only locally owned original data. Compared to the centralized model performed with the same physical and temporal resources, about 98% of performance was achieved.