Engineering, 2013, 5, 37-41
doi:10.4236/eng.2013.55B008 Published Online May 2013 (http://www.scirp.org/journal/eng)
Wireless Bioradar Sensor Networks for Speech Detection
Ying Tian, Sheng Li, Jianqi Wang*
College of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China
Wireless multimedia sensor networks (WMSN) are emerging to serve for the collection of acoustic and image informa-
tion. In the WMSN, the microphone is usually employed to function as sensor nodes for the acquisition of acoustic data.
However, those microphone sensors are needed to be placed close with sound source and cannot detect sound signal
through certain obstacles. To overcome the shortcomings of microphone sensor, we develop a new type of bioradar
sensor to achieve non-contact speech detection and investigate theoretically the mechanism of bioradar for speech de-
tection. Results show that the system can successfully detect speech at some distance and even through non-metallic
objects with certain thickness. In addition, in order to suppress the noise and improve the quality of the detected speech,
we use spectral subtraction and Wiener filtering algorithm respectively to enhance the bioradar speech and evaluate the
performance of the two methods using spectrogram.
Keywords: Bioradar Sensor; Speech Detection; Wireless Sensor Networks
The availability of low-cost acoustic and image sensors
allows for the emergence of wireless multimedia sensor
networks (WMSN) , which can sense and transmit not
only scalar sensor data but also still images, video and
audio streams through multihop wireless links between
sensors . The WMSN usually uses the microphone to
retrieve audio information. However, those microphone
sensors are needed to be placed close with sound source
and cannot detect sound signal through certain obstacles.
Since the deployment environment is complex and di-
verse for various WMSN applications, the obstacles in
the open area and the detection distance are the critical
factors for consideration when detecting sound signal.
The mechanism of microphone sensor limits its applica-
tion in the area of human speech detection for those spe-
cial environments. Therefore we propose to employ bio-
radar sensor for speech detection.
The bioradar, which can emit electromagnetic wave
and extract information from echo signal in a noninva-
sive, safe, fast, portable fashion, has raised the interest of
research in its medical application such as heart beat and
respiration monitoring, and early stage breast cancer de-
tection [3-5]. Among its promising medical applications,
some reports on speech related topics further confirm the
feasibility of using bioradar as sensor nodes for speech
detection. For example, Holzrichter et al. use very low
power electromagnetic (EM) wave sensors to measure
speech articulator motions as speech is produced . Li
discovers that using 40 GHz dielectric integrated radar
can detect and identify out exactly the existential speech
signals in free space from a person speaking .
In this paper, we investigate theoretically the mecha-
nism of bioradar for speech detection and develop a 35.5
GHz microwave bioradar which can detect speech at
some distance by emitting radio waves in the microwave
range to a target subject and extracting speech informa-
tion from echo signal. In addition, in order to suppress
the noise and improve the quality of the detected speech,
we use spectral subtraction, and Wiener filtering algo-
rithm respectively to enhance the bioradar speech and
evaluate the performance of the two methods using spec-
2. System Architecture
As shown in Figure 1, the wireless bioradar sensor net-
work is a pivotal part of the multi-tier speech detection
system. The lowest tier includes a number of bioradar
sensors and relay sensors. The resources in those sensors
are generally very limited in terms of power, memory
and computational capability. Hence the bioradar sensor
mainly deals with detection of speech and some simple
processing. Due to the output of the bioradar front end is
Copyright © 2013 SciRes. ENG
Y. TIAN ET AL.
analog signal along with noises and very weak, we de-
sign a preprocessing circuit board to improve the sig-
nal-to-noise ratio and convert the analog signal to digital
signal. The communication capability of the bioradar
sensor is very limited, and it cannot communicate with
gateway by a single-hop mode. Hence we design relay
sensors to transmit the data collected from the sensor
node by a multi-hop mode. The relay sensor only forwards
packets. Advanced processing is left to the upper tier.
The second tier is gateway. It provides a transparent
interface to the wireless bioradar sensor network and an
interface to central server via Internet. Depending on the
end-user application, we can employ corresponding de-
vice, such as CDMA gateway, WLAN gateway or GPRS
gateway. The capability of gateway in computation,
storage, and communication is greater than the bioradar
sensor and relay sensor. It is responsible for a number of
tasks, including sensor registration, initialization, cus-
tomization, time synchronization, data retrieval and
processing, and data fusion .
The third tier is central server where the large amount
of data collected can be interpreted and analyzed. It can
implement algorithms to identify an unexpected situation
from the speech information and trigger required actions.
In addition, the central server provides a graphical inter-
face for real-time monitoring and also an interface for the
definition and configuration of the system’s overall be-
3. Theoretical Analysis
As a mechanical effect, sound is essentially the passage
of pressure fluctuations through an elastic medium as the
result of vibrational forces acting on that medium .
Speech as a type of sound source not only possesses the
physical attributes as other sounds do, but also has
unique attributes related with physiology, since it origi-
nates from the motion of human vocal apparatus. As
shown from the mid-sagittal plane of vocal tract in Fig-
ure 2, speech is the acoustic product of the coordinated
operation of lung, trachea, larynx, pharynx, nose and
mouth. With the contraction of the rib cage and increase
of lung pressure, air is forced from the lungs to pass
through the trachea and get to larynx, where it causes the
elastic vocal folds to vibrate and produces a quasi-peri-
odic train of air pulses to excite the acoustic system. This
pulse train is modified by the resonances of the vocal
tract according to its change in cross-sectional area due
to the movement of the articulators (e.g., the lips, jaw,
tongue and soft palate, producing distinguishable voice
sounds . This vibration information of sound can be
detected by bioradar due to the doppler effect .
If is an ideal transmitted signal of continuous
waveforms (CW) radar, then
Figure 1. Speech detection system architecture.
Figure 2. Diagram of mid-sagittal plane of vocal tract.
where A is the amplitude of the transmitted signal, 0
the frequency of the transmitted signal and 0
initial phase. Because of the production of speech caus-
ing vibration and in turn causing the doppler effect on the
received signal, its phase is changed. We denote the
change of phase by ()t
, and thus the received signal
can be written by
= attenuation coefficient and2/Rc
. R is
the target distance and c is the velocity of light. In the
mixter, the transmitted signal is mixed with the received
signal, and we get the mixed signal
After filtering out the high-frequency and DC compo-
nent, we obtain
where K is the gain of mixing and filtering. Because the
change of phase is small and sin() ()tt
, equation (4) can be more simply written as
The varying displacement of vibration, ()t
arly proportional to the varying phase of thsignal: e echo
Copyright © 2013 SciRes. ENG
Y. TIAN ET AL.
Copyright © 2013 SciRes. ENG
ing antennas are both parabolic antennas with a diameter
of 300 mm, and the estimated beam width is 9°. The
voltage controlled oscillator generates a very stable MMW
at 34.5 GHz with an output power of 100 mW .
is the wavelength of b
ioradar signal. Substi-
tuting E. (6) into Eq. (5), we obtain
4.2. Process Unit
() ()St Kt
This theoretical analysis suggests th
ar front end is shown in Figure 4.
The output speech signal of the bioradar front end has
high frequency and relatively low amplitude. In order to
suppress noises improving signal-to-noise ratio and to
convert the analog output to digital signal, we design a
preprocessing circuit as shown in Figure 5.
at the echo signal
n be modulated by the vibration originating from the
production of speech, thereby causing the change of its
phase. This varying phase forms the base of the extrac-
tion of life parameter related with speech.
4. Bioradar Sensor Hardware Design
Preamplifier performs impedance matching between
the output of bioradar front end and the input of the next
circuit. It provides protection and insulation for the cir-
cuit. To meet the design requirements on stopband at-
tenuation, we employ higher order filtering. Finally, to
eliminate 50 Hz power-line interference, a 50 Hz notch
filter is designed.
Based on the modular design theory, the biorad
can be separated into three modules according to their
specific functions, as shown in Figure 3. The function of
sensor module is speech data acquisition; process unit
plays the role of adjusting the signal to the back end ap-
plication; radio module mainly deals with the transmis-
sion of speech data wirelessly. The details for each mod-
ule design are given in the following section.
4.1. Sensor Module
In addition, to improve the detection precision when
performing AD conversion, we do not employ the
built-in 14-bit ADC of CC2430, but rather choose an
A/D chip, AD7792 with higher precision. The AD7792 is
a low power, complete analog front end. It contains a low
noise 16-bit Σ-Δ ADC with three differential analog in-
The design of the biorad
In this bioradar front end, a superheterodyne receiver is
employed. The advantage of using such receiver is that it
employs two-step indirect-conversion transceiver, so that
to mitigate the severe DC offset problem and the associ-
ated 1/f noise at baseband, that occurs normally in the
direct-conversion receivers. The transmitting and receiv- Figure 3. Block diagram of the bioradar sensor.
Figure 4. The schematic diagram of the bioradar front end.
Y. TIAN ET AL.
Figure 5. Block diagram of preprocessing circuit.
Figure 6. Interface of AD7792 and CC2430.
Figure 7. Spectrogram of bioradar speech. (a) speechroc-
de. To reduce the power consump-
we choose CC2430 chip.
d to speak seven
omputer. The area
collection in WMSN
uirement of speech detection in some
fore we propose to use bioradar sen-
essed by spectral subtraction. (b) speech processed by Wie-
Energy consumption is one of the major concerns when
4.3. Radio Module
designing the sensor no
tion and cost of sensor node,
The CC2430 is the first System-on-Chip (SoC) solution
for ZigBee and it combines the excellent performance 2.4
GHz DSSS RF transceiver core with an industry-standard
enhanced 8051 MCU. The CC2430 is highly suited for
systems where ultra low power consumption is required.
This is ensured by various operating modes. Short transi-
tion times between operating modes further ensure low
power consumption .
Here CC2430 communicates with AD7792 using SPI
(Serial Peripheral Interface) mode as shown in Figure 6.
Bioradar Speech Denoising
In our experiments, the bioradar sensor was positioned in
front of the speaker and he was aske
sentences, which were recorded in a c
of the room for the experiment is 172.5 square meters.
Result showed the bioradar speech was disturbed by
noise. To reduce the noise and improve the quality of
bioradar speech, we employed spectral subtraction an
iener filtering algorithm respectively to enhance the
detected speech. To evaluate the performance of the two
methods, we plot spectrogram using Matlab. The result is
shown in Figure 7. It is observed that at the nonspeech
segments Wiener filtering is superior to spectral subtrac-
tion in denoising, whereas at the speech segments al-
though Wiener filtering is still more effective in suppres-
sion of noise than spectral subtraction, it also removes
some frequency components thereby causing the loss of
speech information. This suggests that neither Wiener
filtering nor spectral subtraction is optimal for the reduc-
tion of noise of bioradar speech, and therefore we need to
explore another algorithm to suppress the noise while
preserve the speech information as complete as possible
not distorting the speech signal.
Microphone-based audio stream
cannot meet the req
sor as a new method to establish wireless bioradar sensor
network for speech detection. The penetrability of biora-
dar allows it to detect speech at some distance and even
through non-metallic objects with certain thickness. The
new bioradar network extends the WMSN application as
well as providing us with richer information.
The work was supported by National Key T
Copyright © 2013 SciRes. ENG
Y. TIAN ET AL. 41
Research and Development Program of the Ministry of
 M. Guerrero-Zapata, R. Zilan, J. M. Barceló-Ordinas, K
Bicakci and B.ecurity in W
Multimedia Sommunication Sys-
Science and Technology of China (No. 2012BAI20B02).
Tavli, “The Future of S
ensor Networks,” Telec ireless
tems, Vol. 45, No. 1, 2010, pp. 77-91.
 L. You and C. G. Liu, “Robust Cross-layer Design of
Wireless Multimedia Sensor Networks
and Uncertainty,” Journal of Ne with Correla
tworks, Vol. 6, No. 7,
2011, pp. 1009-1016. doi:10.4304/jnw.6.7.1009-1016
 J. Q. Wang, C. X. Zheng, G. H. Lu and X. J. Jing, “A
New Method for Identifying the Life Parameters via Ra-
dar,” EURASIP Journal on Applied Signal Processing,
Vol. 2007, No. 1, 2007, pp. 16-16.
 H. Lv, G. H. Lu, X. J. Jing and J. Q. Wang, “A New Ul-
tra-Wideband Radar for Detecting Su
Earthquake Rubbles,” Mrvivors Buried under
icrowave and Optical Technol-
ogy Letters, Vol. 52, No. 11, 2010, pp. 2621-2624.
 S. K. Davis, B. D. Van Veen, S. C. Hagness and F. Kelcz,
“Breast Tumor Characterization Based on Ultrawid
Biomedical Engineering, IEEE
Transactions on, Vol. 55, No. 1, 2008, pp. 237-246.
 J. Holzrichter, G. Burnett, L. Ng and W. Lea, “Speech
Articulator Measurements Using Low Power EM-wave
Sensors,” Journal of the Acoustical Society of America,
Vol. 103, 1998, pp. 622-625.doi:10.1121/1.421133
 Z. W. Li, “Millimeter Wave Radar for Detecting the
Speech Signal Applications,” International Journal of In-
frared and Millimeter Waves, Vol. 17, No. 12, 1996, pp.
 A. Milenković, C. Otto and E. Jovanov, “Wireless Sensor
Networks for Personal Health Monitoring: Issues and an
Implementation,” Computer Communications, Vol. 29,
No. 13, 2006, pp. 2521-2533.
 H. Alemdar and C. Ersoy, “Wireless Sensor Networks for
Healthcare: A Survey,” Computer Ne
tworks, Vol. 54, No.
15, 2010, pp. 2688-2710.
 D. R. Raichel, “The Scien
tics,” Springer Science+Business
ce and Applications of Acous-
Media, New York,
h Analysis Synthesis and Perception,” Springer,
ing, “A New Kind of Non-Acoustic Speech
7793, Analog Devices, Inc.
 J. L. Flanagan, J. B. Allen and M. A. Hasegawa-Johnson,
 M. I. Skolnik, “Introduction to Radar System,”McGraw-
 S. Li, Y. Tian, G. Lu, Y. Zhang, H. J. Xue, J. Q. Wang
and X. J. J
Acquisition Method Based on Millimeter Wave Radar,”
Progress In Electromagnetics Research, Vol. 130, 2012,
 AD7792 datasheet, Preliminary Technical Data
 CC2430 datasheet, A True System-on-Chip solution for
2.4 GHz IEEE 802.15.4 / ZigBee™, Texa
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