Signal Processing - November 2016 - 25
balance changes to increase a parasympathetic activity, the
The signal enhancement function distinguishes the QRS
RR interval decreases and the driver workload increases
complex from a distorted QRS complex by variability,
(driver wakes up).
abnormalities, low SNR, and artifacts [22]. This function
makes use of the amplitude and slope of the R-peak as characteristic features, and a combination of Hilbert transforAnalog signal processing for DSM systems
mation (HT), mathematical morphology (MM), empirical
using physiological signals
mode decomposition (EMD), and filter banks (FBs) can be
Automobile manufacturers such as Toyota, Ford, BMW,
used; HT is used to extract the signal envelope, which is
and Daimler AG have been developing DSM systems using
a characteristic feature of the ECG, MM shows excellent
the ECG and PPG of the driver since the late 2000s [6], and
performance in reducing the impact of motion artifacts and
some of these DSM systems are applied to actual vehicles.
line drifts, and EMD and FBs convert the QRS signal into a
In 2010, BMW jointly developed a steering wheel with Techfrequency domain and decompose the entire spectrum into
nische Universitaet Muenchen as part of the Fit4Age Project.
subband spectrum.
The developed steering wheel features contact-based signal
The driver status analysis function measures the heartbeat
acquisition using a strip-type skin-resistance sensor and a
interval (the interval between R-peaks, also known as the RR
punctiform-type reflective pulse oximetry PPG sensor, which
interval). The heart rate variability (HRV) technique [7] uses
are installed in the BMW 730d [10]. In the system shown in
variations of the measured RR intervals to
Figure 1, two conductive strip electrodes
analyze a driver's autonomic nervous activare attached all around the steering wheel
Slow-acting sympathetic
ity in either the time or frequency domain
to measure the driver's skin resistance.
activity may increase
[23]. The analysis in the time domain utiBecause the resistance acquired from the
lizes mean RR (seconds), standard deviation
driver's skin depends on how the driver
the HR, while fast-acting
of the RR interval (SDNN) (milliseconds),
holds the steering wheel, an electrodermal
parasympathetic activity
mean heart rate (HR) (beats/minute), stanactivity (EDA) circuit [26] that applies an
decreases the HR.
dard deviation of the HR (Std RR) (milautomatic bias control using two operaliseconds), and the root mean square of
tional amplifiers is applied to increase the
successive heartbeat interval differences (RMSSDs) (millisecdynamic range of sensors [10]. A real driving test of a BMW
onds) to estimate the effect of the autonomic nervous system on
730d equipped with the developed steering wheel is conducted
HR [7]. On the other hand, the frequency-domain analysis is
by 21 test drivers of average age 65. These participants drove
classified into parametric and nonparametric methods [7]. The
the vehicle three times during 10 minutes on a 16-km long
parametric and nonparametric methods estimate the power
preselected route (highways, state roads, and urban areas) to
spectrum density (PSD) by means of the autoregressive (AR)
evaluate the actual performance of the developed steering
model and fast Fourier transform (FFT) [25], respectively. The
wheel [9]. From the driving test, it is found that approximately
parametric method computes the power in low frequency (LF)
81% of the meaningful measurements of skin resistance were
and that in high frequency (HF) ( LFAR 6ms 2@, HFAR 6ms 2@),
obtained from the strip-type sensor, while 44% of the valid
measurements of PPG were observed from the punctiformand the percentage of the power in the low- and high-frequentype sensor. One study [9] reported that employing a stripcy segments ( LFAR 6%@ and HFAR 6%@ , respectively) in the
type sensor is suitable for commercializing the developed
parameterized FFT spectrum. Similarly, the nonparametsteering wheel by BMW.
ric method estimates the power in low and high frequencies
From 2008 to 2011, Denso, in collaboration with Toyota
( LFFFT 6ms 2@, HFFFT 6ms 2@ ), and the percentage of the power
and Nippon Medical University, developed a special steering
in low- and high-frequency segments ( LFFFT 6%@, HFFFT 6%@ )
wheel that can measure (acquire) ECG and PPG [17]-[19]. For
in the FFT spectrum [7]. The frequency ranges of 0.04-0.15 Hz
ECG acquisition, positive and negative electrodes are attached
and 0.15-0.4 Hz are used for low- and high-frequency segto the right and left sides of the steering wheel, respectively, and
ments, respectively.
chrome-coated metal electrodes with high input impedance
Slow-acting sympathetic activity may increase the HR,
( 90, 000 X ) are used. From the steering wheel, 1~5 mV
while fast-acting parasympathetic activity decreases the
HR. A sympathetic activity influences the power in both
ECG is measured through the electrodes and amplified
low and high frequencies, whereas a parasympathetic activabout 1,700 times. For PPG acquisition, a 525-nm green
ity makes an effect on the power only in high frequency.
LED is attached on the steering wheel, which is widely used
The balance between the effects of the sympathetic and
to reduce the surface reflection from the skin. The baseline
parasympathetic activities is referred to as the sympathovawander, considered as an artifact caused by perspiration, resgal balance, which can be measured by the ratio of powers
piration, body movements, and unstable contact with elecin LF to that in HF [23]. It is found that when the sympatrodes, is minimized after the acquired ECG and PPG pass
thovagal balance changes to increase a sympathetic activthrough an HPF ( fc = 0.3 Hz for both ECG and PPG) [18].
ity, the RR interval increases, and the driver status analysis
Similarly, noise is minimized using an LPF ( fc = 35 Hz for
function detects that the driver workload decreases (driver
ECG and fc = 30 Hz for PPG ), and then, the measured ECG
falls asleep). On the other hand, when the sympathovagal
and PPG are digitalized by an analog-digital converter (ADC)
IEEE SIgnal ProcESSIng MagazInE
|
November 2016
|
25
Table of Contents for the Digital Edition of Signal Processing - November 2016
Signal Processing - November 2016 - Cover1
Signal Processing - November 2016 - Cover2
Signal Processing - November 2016 - 1
Signal Processing - November 2016 - 2
Signal Processing - November 2016 - 3
Signal Processing - November 2016 - 4
Signal Processing - November 2016 - 5
Signal Processing - November 2016 - 6
Signal Processing - November 2016 - 7
Signal Processing - November 2016 - 8
Signal Processing - November 2016 - 9
Signal Processing - November 2016 - 10
Signal Processing - November 2016 - 11
Signal Processing - November 2016 - 12
Signal Processing - November 2016 - 13
Signal Processing - November 2016 - 14
Signal Processing - November 2016 - 15
Signal Processing - November 2016 - 16
Signal Processing - November 2016 - 17
Signal Processing - November 2016 - 18
Signal Processing - November 2016 - 19
Signal Processing - November 2016 - 20
Signal Processing - November 2016 - 21
Signal Processing - November 2016 - 22
Signal Processing - November 2016 - 23
Signal Processing - November 2016 - 24
Signal Processing - November 2016 - 25
Signal Processing - November 2016 - 26
Signal Processing - November 2016 - 27
Signal Processing - November 2016 - 28
Signal Processing - November 2016 - 29
Signal Processing - November 2016 - 30
Signal Processing - November 2016 - 31
Signal Processing - November 2016 - 32
Signal Processing - November 2016 - 33
Signal Processing - November 2016 - 34
Signal Processing - November 2016 - 35
Signal Processing - November 2016 - 36
Signal Processing - November 2016 - 37
Signal Processing - November 2016 - 38
Signal Processing - November 2016 - 39
Signal Processing - November 2016 - 40
Signal Processing - November 2016 - 41
Signal Processing - November 2016 - 42
Signal Processing - November 2016 - 43
Signal Processing - November 2016 - 44
Signal Processing - November 2016 - 45
Signal Processing - November 2016 - 46
Signal Processing - November 2016 - 47
Signal Processing - November 2016 - 48
Signal Processing - November 2016 - 49
Signal Processing - November 2016 - 50
Signal Processing - November 2016 - 51
Signal Processing - November 2016 - 52
Signal Processing - November 2016 - 53
Signal Processing - November 2016 - 54
Signal Processing - November 2016 - 55
Signal Processing - November 2016 - 56
Signal Processing - November 2016 - 57
Signal Processing - November 2016 - 58
Signal Processing - November 2016 - 59
Signal Processing - November 2016 - 60
Signal Processing - November 2016 - 61
Signal Processing - November 2016 - 62
Signal Processing - November 2016 - 63
Signal Processing - November 2016 - 64
Signal Processing - November 2016 - 65
Signal Processing - November 2016 - 66
Signal Processing - November 2016 - 67
Signal Processing - November 2016 - 68
Signal Processing - November 2016 - 69
Signal Processing - November 2016 - 70
Signal Processing - November 2016 - 71
Signal Processing - November 2016 - 72
Signal Processing - November 2016 - 73
Signal Processing - November 2016 - 74
Signal Processing - November 2016 - 75
Signal Processing - November 2016 - 76
Signal Processing - November 2016 - 77
Signal Processing - November 2016 - 78
Signal Processing - November 2016 - 79
Signal Processing - November 2016 - 80
Signal Processing - November 2016 - 81
Signal Processing - November 2016 - 82
Signal Processing - November 2016 - 83
Signal Processing - November 2016 - 84
Signal Processing - November 2016 - 85
Signal Processing - November 2016 - 86
Signal Processing - November 2016 - 87
Signal Processing - November 2016 - 88
Signal Processing - November 2016 - 89
Signal Processing - November 2016 - 90
Signal Processing - November 2016 - 91
Signal Processing - November 2016 - 92
Signal Processing - November 2016 - 93
Signal Processing - November 2016 - 94
Signal Processing - November 2016 - 95
Signal Processing - November 2016 - 96
Signal Processing - November 2016 - 97
Signal Processing - November 2016 - 98
Signal Processing - November 2016 - 99
Signal Processing - November 2016 - 100
Signal Processing - November 2016 - 101
Signal Processing - November 2016 - 102
Signal Processing - November 2016 - 103
Signal Processing - November 2016 - 104
Signal Processing - November 2016 - 105
Signal Processing - November 2016 - 106
Signal Processing - November 2016 - 107
Signal Processing - November 2016 - 108
Signal Processing - November 2016 - 109
Signal Processing - November 2016 - 110
Signal Processing - November 2016 - 111
Signal Processing - November 2016 - 112
Signal Processing - November 2016 - 113
Signal Processing - November 2016 - 114
Signal Processing - November 2016 - 115
Signal Processing - November 2016 - 116
Signal Processing - November 2016 - 117
Signal Processing - November 2016 - 118
Signal Processing - November 2016 - 119
Signal Processing - November 2016 - 120
Signal Processing - November 2016 - 121
Signal Processing - November 2016 - 122
Signal Processing - November 2016 - 123
Signal Processing - November 2016 - 124
Signal Processing - November 2016 - 125
Signal Processing - November 2016 - 126
Signal Processing - November 2016 - 127
Signal Processing - November 2016 - 128
Signal Processing - November 2016 - 129
Signal Processing - November 2016 - 130
Signal Processing - November 2016 - 131
Signal Processing - November 2016 - 132
Signal Processing - November 2016 - 133
Signal Processing - November 2016 - 134
Signal Processing - November 2016 - 135
Signal Processing - November 2016 - 136
Signal Processing - November 2016 - 137
Signal Processing - November 2016 - 138
Signal Processing - November 2016 - 139
Signal Processing - November 2016 - 140
Signal Processing - November 2016 - 141
Signal Processing - November 2016 - 142
Signal Processing - November 2016 - 143
Signal Processing - November 2016 - 144
Signal Processing - November 2016 - 145
Signal Processing - November 2016 - 146
Signal Processing - November 2016 - 147
Signal Processing - November 2016 - 148
Signal Processing - November 2016 - Cover3
Signal Processing - November 2016 - Cover4
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