TY - GEN
T1 - Machine Learning-based Wearable Bio-processor for Real-Time Blood Pressure Estimation
AU - Yoon, Jee Ye
AU - Kim, Hayeon
AU - Ham, Eun Gyeong
AU - Yang, Hannah
AU - Kim, Ji Hoon
N1 - Funding Information:
This research was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No. 2020‐ 0 ‐ 01308, Intelligent Mobile Processor based on Deep-Learning Micro Core Array)
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Recently, as continuous monitoring of blood pressure (BP) becomes important for the treatment and management of hypertension, which is a representative chronic disease, many studies on non-invasive cuff-less BP have been conducted. Due to accuracy degradation of the simple linear algorithms, complex nonlinear algorithms are preferred, but its latency and energy efficiency are suffered from the limited CPU computational capability on wearable devices. In this paper, we present a wearable bio-processor that is capable of real-time BP estimation based on Electrocardiogram (ECG) and Photoplethysmography (PPG), which can provide high BP estimation accuracy with a nonlinear regression machine learning model. The proposed bio-processor including Arm Cortex-M0 and dedicated hardware accelerators runs at 50MHz and is prototyped with a Xilinx Artix-7 FPGA. It shows the root mean square error (RMSE) of 6.04 mmHg and 5.88 mmHg for Systolic BP and Diastolic BP, respectively.
AB - Recently, as continuous monitoring of blood pressure (BP) becomes important for the treatment and management of hypertension, which is a representative chronic disease, many studies on non-invasive cuff-less BP have been conducted. Due to accuracy degradation of the simple linear algorithms, complex nonlinear algorithms are preferred, but its latency and energy efficiency are suffered from the limited CPU computational capability on wearable devices. In this paper, we present a wearable bio-processor that is capable of real-time BP estimation based on Electrocardiogram (ECG) and Photoplethysmography (PPG), which can provide high BP estimation accuracy with a nonlinear regression machine learning model. The proposed bio-processor including Arm Cortex-M0 and dedicated hardware accelerators runs at 50MHz and is prototyped with a Xilinx Artix-7 FPGA. It shows the root mean square error (RMSE) of 6.04 mmHg and 5.88 mmHg for Systolic BP and Diastolic BP, respectively.
KW - Bio-processor
KW - Blood Pressure
KW - BP Estimation
KW - Machine Learning
KW - SoC (System-on-Chip)
UR - http://www.scopus.com/inward/record.url?scp=85128812277&partnerID=8YFLogxK
U2 - 10.1109/ICEIC54506.2022.9748830
DO - 10.1109/ICEIC54506.2022.9748830
M3 - Conference contribution
AN - SCOPUS:85128812277
T3 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
BT - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Electronics, Information, and Communication, ICEIC 2022
Y2 - 6 February 2022 through 9 February 2022
ER -
