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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.13 No.2
- 발행연도
- 2023.5
- 수록면
- 221 - 233 (13page)
- DOI
- https://doi.org/10.1007/s13534-023-00271-1
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초록· 키워드
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The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure theirblood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearabledevices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulsetransit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration usingarm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-basedBP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wristcuffBP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolicblood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitoremploying recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regressionapproaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate asthat obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the firststudy to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliablysubstitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as wellas frequent calibrations to ensure accurate BP monitoring, are now feasible.
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