|Title:||A Blood Pressure Monitoring Device with Tactile and Tension Sensors Assisted by a Machine Learning Technique||Authors:||Tan, Fu
Huang, Kuan Hua
|Keywords:||blood pressure estimation | Conductive polymer | machine learning | pressure-sensing array | tension sensor||Issue Date:||1-Jun-2019||Source:||2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems and Eurosensors XXXIII, TRANSDUCERS 2019 and EUROSENSORS XXXIII||Abstract:||
© 2019 IEEE. This work presents the development of a continuous blood pulse-wave monitoring system with a highly sensitive tactile sensing array and tension sensor. The key element of the sensing device is a conductive polymer film that is patterned with microdome structures to enhance pressure sensitivity. The proposed array-type configuration greatly facilitates the measurement of blood pulse waves. In addition, the tension sensor, which detects the strap tension during pulse wave measurement, is capable of estimating the optimal conditions for measuring high-quality blood pulse waves. Furthermore, a machine-learning algorithm, the support vector regression, is employed for estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) from the measured pulse-wave signals. The R2 between the estimated SBP and the reference SBP was 0.724, and the R2 between the estimated DBP and the reference DBP was 0.769.
|Appears in Collections:||機械工程學系|
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