Continuous Non-Invasive Blood Pressure Measurement Using 60GHz-Radar – A Feasibility Study

Type: MA thesis

Status: finished

Date: April 1, 2022 - September 30, 2022

Supervisors: Merlin Nau, Andreas Maier, Jens Harnisch (Infineon)

Hypertension – high blood pressure (BP) – is known to be a silent killer. Untreated, it can cause
severe damage to the human’s organs, mainly to the heart and kidneys [5, 6]. BP is
usually classified by using the highest – systolic – and the lowest – diastolic – pressures during one
cardiac cycle [2]. The gold standard for measuring BP remains the oscillometric method,
which is employed in traditional arm-cuffs [4]. This method, however, suffers from extensive
deficiencies: Discomfort leads to unreliable measures [2]. Additionally, it only captures
the static status of the very dynamic arterial BP and thus loses important variation information,
leading to poor time resolution [2, 3, 4, 7] However, there is a strong
need for continuous beat-to-beat BP readings [4], as they are more reliable predictors of
aforementioned cardiovascular risks than single readings [1].
The goal of this master thesis is to show whether it is feasible to use a 60GHz radar device to
continuously estimate BP. Radar is chosen as it has a very small form factor and very low power
consumption – both being favorable characteristics for integrating into a wearable device. The
radar is put into an 3D-printed enclosure which is fastened to the left wrist using a velcro strap. It
is capable of extracting the skin displacement caused by the expansion of the underlying artery,
which is localized using a beamforming algorithm. The extracted skin displacement contains the
pulse waveforms which are used for extracting the BP.
In literature, mainly two methods have been used to design continuous BP devices. One is based
on Pulse-Wave-Velocity, and in that context also Pulse-Transit-Time, the other is based on Pulse-
Wave-Analysis [4]. Since the first method depends on the usage of an electrocardiograph,
this method was not employed in this work, as the goal is to implement a stand-alone solution
which does not require additional devices. Therefore, the second method is implemented.
For that, the extracted skin displacement is split into individual pulse waveforms. Each is used
as input for a support vector machine, that decides whether it is good enough as an input for the
neural network, such that only sufficiently good waveforms are used. Then, 21 distinctive features
are extracted for the individual good waveforms. These features, together with the calibration
parameters gender, age, height and weight, are used as features for a neural network. The network
is then used to predict systolic and diastolic values.
It is expected that some correlation between the skin displacement, captured by the radar, and
the corresponding BP will become apparent, allowing for future research to further improve the



[1] D. Buxi, J.-M. Redout´e, and M. R. Yuce. Blood pressure estimation using pulse
transit time from bioimpedance and continuous wave radar. IEEE Transactions on
Biomedical Engineering, 64(4):917–927, 2016.
[2] Y. Kurylyak, F. Lamonaca, and D. Grimaldi. A neural network-based method for
continuous blood pressure estimation from a ppg signal. In 2013 IEEE International
instrumentation and measurement technology conference (I2MTC).
IEEE, 2013.
[3] M. Proenc¸a, G. Bonnier, D. Ferrario, C. Verjus, and M. Lemay. Ppg-based blood
pressure monitoring by pulse wave analysis: calibration parameters are stable for three
months. In 2019 41st Annual International Conference of the IEEE Engineering in
Medicine and Biology Society (EMBC), pages 5560–5563. IEEE, 2019.
[4] J. Sol`a and R. Delgado-Gonzalo. The handbook of cuffless blood pressure monitoring.
Springer. Available online at: https://link. springer. com/book/10, 1007:978–3, 2019.
[5] WHO. Hypertension.World Health Organization. URL:
[6] X. Xing, Z. Ma, M. Zhang, Y. Zhou, W. Dong, and M. Song. An unobtrusive and
calibration-free blood pressure estimation method using photoplethysmography and
biometrics. Scientific reports, 9(1):1–8, 2019.
[7] Y. Yoon, J. H. Cho, and G. Yoon. Non-constrained blood pressure monitoring using
ecg and ppg for personal healthcare. Journal of medical systems, 33(4):261–266, 2009.