1. Heart Rate Estimation of Car Driving
Using Radar Sensors and
Blind Source Separation
Keito Murata, Daichi Kitamura, Ryo Saito, Daichi Ueki
14th Asia Pacific Signal and Information Processing Association Annual Summit and Conference
Session: SS14: Emerging signal Processing Technology for Medical Applications/
Biomedical Signal Processing and Systems
Time: Wed., 9. Nov, 17:20-17:40 (UTC +7)
† †
National Institute of Technology, Kagawa College, Japan
Murata Manufacturing Co., Ltd.
†

2. • Background
– Some situations are fatal while driving
• Sleeping
• Sudden seizure such as myocardial infarction
• Lose consciousness
– Needs the system always monitors the driver’s heart rate
• Research summary and purpose
– Using a contactless millimeter-wave radar sensor
• Less burden for driver
• No need to wear
• Hygienic
– Measuring displacement of the driver’s body
surface using the radar sensor
– We analyze components of heartbeat from the observed signal
Radar sensor
Research summary
2

3. • Problem
– Displacement of breathing and driver’s movement except heart rate is
measured simultaneously
– Relative position of radar sensor and body surface is unknown
• Our approach
– Separate heart rate signal from noise signal (breathing, driver’s
movement, vibration of car body) with blind source separation (BSS)
– Analyzes heart rate from separated heartbeat signal
– Evaluates by degree of agreement with heart rate obtained by medical
electrode pad
Research summary
3
Contact-type
electrocardiograph (ECG)
sensor

4. • Measurement simulating car vibration while driving
– Vibrates the vibration plate with sin wave in vertical direction
– Measures a microdisplacement of the driver’s body surface with radar
sensor embedded in the back of the seat
• Condition of vibration plate
Measurement system in this research
4
ECG sensor
Vibration
direction
Radar sensor
Amp.
[mm]
Time [s]
0 60 360 420
Vibration time section
Frequency：1.2 [Hz]
Vibration plate

5. • Observed signal of 4 channels directional beam
– Measures a 4points near the displacement of driver’s body surface
– Sampling frequency：40 [Hz]
5
Observed signal of radar sensor
Vibration time of vibration plate
Displacement
of
driver’s
body
surface
[
m
m]
Time [s]
Ch.
1
Ch.
2
Ch.
3
Ch.
4

6. • Time-frequency structure (spectrogram)
Comparison of radar sensor and ECG sensor
6
Time [s] Time [s]
Frequency
[Hz]
Frequency
[Hz]
Radar sensor (Ch. 3) ECG sensor

7. • Time-frequency structure (spectrogram)
Comparison of radar sensor and ECG sensor
6
Time [s] Time [s]
Frequency
[Hz]
Frequency
[Hz]
Radar sensor (Ch. 3) ECG sensor

8. • Apply three processes below
– ①Preprocess, ②BSS, ③Heart rate estimation algorithm
Hear rate
estimation
algorithm
③
Process flow of proposed method
8
Observed
signal
Estimated
heartbeat signal
Estimated
heart rate
Heart
rate
[bpm]
Time [s]
BSS
②
Preprocess
①

9. ②
Hear rate
estimation
algorithm
③
Process flow of proposed method
9
Observed
signal
Estimated
heartbeat signal
Estimated
heart rate
Heart
rate
[bpm]
Time [s]
BSS
Preprocess
①
Preprocess
①

10. 10
• Component of body movement caused by breathing
– Remove breathing by filtering
before applying BSS
• Specification of digital filter
– High-pass FIR filter
• Cut-off frequency: 1.5 Hz
• Tap length：170
Frequency [Hz]
Amp.
[dB]
①Preprocess
Before applying filter After applying filter
Amplitude responses
Frequency
[Hz]
Frequency
[Hz]
Time [s] Time [s]

11. Hear rate
estimation
algorithm
③
Process flow of proposed method
11
Observed
signal
Estimated
heartbeat signal
Estimated
heart rate
Heart
rate
[bpm]
Time [s]
Preprocess
①
BSS
②

12. • Assumptions
– Relative locations of body surface and radar sensor is unknown
(mixing system is unknown)
– Exploit BSS
• Methods of BSS used in this research
– Independent vector analysis: IVA [Kim+, 2006]
– Independent low-rank matrix analysis: ILRMA [Kitamura+, 2016]
– ILRMA based on Student- distribution: -ILRMA [Mogami+, 2017]
②BSS
12
Mixing system Demixing system

13. • Independent component analysis: ICA [Comon, 1994]
– estimates demixing matrix under the conditions that mixing
matrix is unknown
– optimizes to maximize independence between original sources
• Maximize non-Gaussian between sources
②BSS: ICA
13
Approaches to Gaussian distribution
by mixture (central limit theorem)
Maximize non-Gaussianity to
estimate the sources (ICA)

14. • ICA for time-frequency domain (spectrogram)
– assume the frequency-wise mixing and demixing matrices
– BSS for convolutive mixtures can be achieved ：Frequency index
：Time index
②BSS: BSS in time-frequency domain
14
Mixing system
Demixing system
Mixing matrix
Source signal
Observed signal
Demixing matrix
Observed signal
Separated signal
Freq.
Freq.
Freq.
Freq.
Time
Time
Time
Time

15. • Independent vector Analysis (IVA) [Kim+, 2006]
– Assumption 1: Maximize independence between
each signal (same as ICA)
– Assumption 2: Co-occurrence of all the frequency
components in the same source
– Observed signal obtained by radar sensor
• Clear harmonic structure exits in heartbeat and
vibration component
• Fundamental frequency and its harmonic
components are simultaneously activated
②BSS: Summary of IVA
15
Observed
signal
Separated
signal
Frequency-wise
demixing
matrix
Estimate
satisfied with
two assumptions
Two assumptions of IVA hold Time [s]
Frequency
[Hz]

16. • Independent low-rank matrix analysis (ILRMA) [Kitamura+, 2016]
– Assumption 1: Maximize independence between
each signal source (same as ICA)
– Assumption 2: Time-frequency structure of each
source tends to be low-rank
• Low-rank: Spectrogram includes similar spectra many times
– Observed signal obtained by radar sensor
• Component of heartbeat and vibration plate
have almost the same spectrum for a long time
②BSS: Summary of ILRMA
16
Observed signal Separated signal
Frequency
wise
demixing
matrix
Two assumptions of ILRMA hold
Estimate
satisfied with
two assumptions
Frequency
[Hz] Time [s]

17. • -ILRMA: Generalized method of ILRMA
– ILRMA assumes the complex Gaussian distribution as a source
generative model
– -ILRMA generalizes the source model by using the complex Student’s
t distribution
– More robust and precise BSS can be achieved by a heavy-tale property
• Complex Student’s distribution
②BSS: Summary of -ILRMA
17
: DoF parameter
: Scale parameter
Equivalent to
Gaussian dist.
heavy tale
dist.
Induces robust
low-rank modeling

18. ②BSS: conditions
18
• Parameter of STFT
• Parameter setting of IVA, ILRMA, and -ILRMA
Parameter IVA ILRMA -ILRMA
Initial value of
demixing matrix ww
Identity matrix
Number of iteration of
Iterative optimization algorithm
100 times
Initial value of and ww ー
Uniform random number
(from 0 to 1)
Number of rank for
each signal source
ー 3
DoF parameter nu ー ー 5
Window length of STFT 1.6 s
Shift length of STFT 0.1 s
Window function Hamming window

19. ②BSS: Comparison of IVA, ILRMA, and -ILRMA
19
IVA ILRMA -ILRMA

20. Process flow of proposed method
20
Observed
signal
Estimated
heartbeat signal
Estimated
heart rate
Heart
rate
[bpm]
Time [s]
BSS
②
Preprocess
① ③
Hear rate
estimation
algorithm
③

21. • Convert separated heartbeat
spectrogram to the
time-domain signal
• In general, we apply R-R interval (RRI) estimation to obtain
heart rates
– RRI estimation does not work due to the residual noise
• We apply another heart rate estimation algorithm below
③Heart rate estimation algorithm
21
Emphasize
heartbeat signal
Band-pass
filter
Detect
spectral peaks
Inverse
STFT
Freq.
[Hz]
Time [s]
Estimated
heart rate
Hear
rate
[bpm]
Time [s]
R wave
RRI

22. ③Results of heart rate estimation
22
IVA’s
Estimated heart rate
ILRMA’s
Estimated heart rate
-ILRMA’s
Estimated heart rate
Heart rate of
ECG sensor
Estimated heart rate of
separated signal
:
:
Vibration plate
working

23. Conclusion
• Problems
– Estimate the driver’s heart rate from observed signal obtained by
contactless sensor
• breathing and driver’s movement noise are measured simultaneously except heart
rate
• Measurement
– Measure microdisplacement of driver’s body surface with radar sensor
• Applications
– Apply BSS (IVA, ILRMA, and -ILRMA)
• The location of the radar sensor, source of heartbeat, body movement, and
breathing are unknown
• Estimated heart rate obtained by -ILRMA shows the highest
separation accuracy
23
Thank you for your attention.