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- W4353053087 abstract "급격한 딥러닝 기술의 발전으로 헬스케어 분야에 딥러닝을 접목시켜 환자의 생체정보나 복합적인 상태를 인식하는 연구가 활발히 수행되고 있다. 특히 통증의 효과적인 관리는 환자의 건강상태 개선 및 의료 만족도의 상승으로 이어진다. 본 논문은 손목 밴드 디바이스를 통해 수집된 IMU 센서 데이터를 입력으로 하는 딥러닝 기반 통증행동 모니터링 시스템을 제안한다. 통증행동의 정밀한 인식을 위해 손 위치와 손동작을 개별 인식하는 2개의 CNN 모델과 통증행동을 일상행동으로 인식하는 오인식을 줄이는 HMM 모델을 결합한 모델을 설계했다. IMU센서 데이터를 수집하는 손목 밴드 디바이스는 저전력 설계를 위해 nRF5240 기반의 MDBT50Q 모듈을 기반으로 설계되었다. 제안하는 시스템은 부산대학교병원의 자문을 받아 선정한 28가지 통증행동을 평균 87.06%의 정확도로 인식한다. With the rapid development of deep learning technology, research on recognizing patients’ biometric information or complex conditions by incorporating deep learning into the healthcare field is being actively conducted. In particular, effective management of pain leads to an improvement in the patient’s health condition and an increase in medical satisfaction. This paper proposes a deep learning-based pain behavior monitoring system with IMU sensor data collected through wristband devices as input. For precise recognition of pain behavior, we designed a combination of two CNN models that individually recognize hand positions and hand movements and an HMM model that reduces misrecognition of pain behavior as daily behavior. The wristband device, which collects IMU sensor data, is designed based on an nRF5240, based MDBT50Q module for low-power design. The proposed system recognizes 28 pain behaviors selected with advice from Pusan National University Hospital with an average accuracy of 87.06%." @default.
- W4353053087 created "2023-03-23" @default.
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- W4353053087 date "2023-02-28" @default.
- W4353053087 modified "2023-10-01" @default.
- W4353053087 title "Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures" @default.
- W4353053087 doi "https://doi.org/10.7840/kics.2023.48.2.227" @default.
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