Authors:
Christian Wiede
1
;
Carolin Wuerich
1
and
Anton Grabmaier
1
;
2
Affiliations:
1
Fraunhofer IMS, Finkenstrasse 61, Duisburg, Germany
;
2
University Duisburg-Essen, Bismarckstrasse 81, Duisburg, Germany
Keyword(s):
Calibration-free Blood Pressure Measurement, Artificial Intelligence, Feature Extraction, Body Scale, PPG, ECG, BCG.
Abstract:
Two health parameters are most relevant for self-monitoring of hypertension: blood pressure and body weight. Blood pressure is normally measured with a blood pressure cuff, whereas body weight can be measured with a simple body scale. If it is possible to integrate blood pressure measurement into easy-to-use body scales, patients will benefit from simpler use and lower overall price. The aim of this work is to develop a body scale with which blood pressure can be measured without calibration and without the need for additional devices. This can be realised by considering surrogate parameters for blood pressure. Starting from sensors such as electrodes, photo diodes and pressure transducers, various biosignals such as ECG, BCG, PPG or bioimpedance are extracted from the sole of the foot. The signal is reduced to morphological features which serve as input to a neural network for blood pressure determination. The integrated artificial intelligence (AI) is to be implemented in an energy
-efficient way on an embedded system. In addition, the energy-efficient implementation ensures battery operation for several months with daily use. Besides the concept, the strengths, weaknesses, threats and opportunities of this concept are examined in detail within the framework of a SWOT analysis. This includes considerations of hardware, software, data and user experience.
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