Journal of Sensor Technology, 2013, 3, 47-56 Published Online September 2013 (
System Architecture and Design Flow of Smart Mobile
Sensing Systems
Won-Jae Yi, Jafar Saniie
Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, USA
Received June 6, 2013; revised July 6, 2013; accepted July 14, 2013
Copyright © 2013 Won-Jae Yi, Jafar Saniie. This is an open access article distributed under the Creative Commons Attribution Li-
cense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This study presents the architecture and design flow of smart mobile sensing systems that performs wireless sensor data
transmission, data analysis and display in real-time. Multiple wireless protocols are used for sensor data transmission
including the Bluetooth, cellular data network and Wi-Fi for Internet access, and Near Field Communication (NFC). An
Android smartphone is utilized to demonstrate the design concept of an Intelligent Personal Communication Node
(iPCN) and to perform real-time sensor data acquisition, processing, analysis, display and transmission. Tested sensors
include acceleration, temperature, electrocardiography (ECG) and phonocardiography (PCG). For computational capa-
bility tests, we have observed the signal processing performance of the smartphone by implementing fast Fourier trans-
form (FFT) of the received ECG signal, and QRS detection algorithm for spontaneous heart beat rate (HBR) estimation.
This system has also been tested for multiple sensor node communication and on-demand sensor data acquisition. The
smart mobile sensing system can also be applied to any environment that requires real-time sensing and wireless remote
Keywords: Android Smartphone; Signal Processing; Wireless Sensor Networks
1. Introduction
Smartphones are the fastest growing component in the IT
market and are the biggest phenomenon of our daily life.
There are more than 100 million smartphone users in the
United States [1] and over 1 billion users throughout the
world [2]. A smartphone has extensive capabilities as a
valuable data communicator and analyzer. A typical An-
droid smartphone embeds an ARM-based processor that
offers sufficient processing power for the operating sys-
tem and a wide variety of applications. Also, a typical
Android device is equipped with multiple wireless adap-
ters including the Internet accessible cellular data net-
work and Wi-Fi adapters, a Bluetooth transceiver, and a
Near Field Communication (NFC) reader. These multiple
wireless technologies are able to manage real-time data
communications on-demand and can be accessed con-
currently without interrupting other protocols.
The smartphone can be applied for delivering a more
efficient and convenient real-time patient health moni-
toring because of its extensive communication and com-
putation capabilities. Examples of patient health moni-
toring attributes include blood pressure, heart beat rate
(HBR), body temperature changes, breathing patterns,
patient movement detections, hemoglobin saturations and
electrocardiography (ECG) [3-6].
The treatment experience for patients and the work ef-
ficiency for medical professionals can be enhanced by
using wireless body sensor networks (BSN). As shown in
Figure 1, a wireless BSN consists of body sensors, a Per-
sonal Communication Node (PCN) and a central com-
puting server for the medical professionals. Wearable
body sensors are desired for mobile system integration
[7,8] and must be designed to consume low power in
order to provide long battery life [9,10] for extended and
continuous patient monitoring. A PCN in the wireless
BSN serves as a real-time data communicator with mul-
tiple wireless protocols [3,9,11-13] to retrieve sensor data
and simultaneous data streaming to a designated central
server. Data received on the central server is stored to
allow medical professionals to remotely access a pa-
tient’s test data, and then using data analysis for patient
diagnosis. For example, an inquiry application can be
implemented on the central server to observe a patient’s
status remotely [14]. The system can be further extended
by integrating GPS (Global Positioning System) data to
track a patient’s position in real-time [15]. In practice,
one central server is sufficient to provide patients’ history
opyright © 2013 SciRes. JST
Figure 1. Wireless body sensor network (BSN) over view.
and diagnosis [16], but it is also possible to integrate
multiple servers to establish a global and comprehensive
infrastructure for a substantial diagnostic database [17].
In this paper, we introduce the system architecture and
design flow of smart mobile sensing systems using An-
droid smartphones. This system is capable of data com-
munications with multiple sensors via the Bluetooth con-
nection, data processing and analysis using the on-board
ARM-based microprocessor, user-friendly data display,
and data streaming to a central server via the Internet
connection. The proposed smart mobile system estab-
lishes all data communications in real-time and can be
easily integrated to any remote monitoring system [18].
This paper is organized as follows: Section 2 illustrates
the overall design flow and the architecture of the smart
mobile system and emphasizes sensor data interpretation
algorithms; Section 3 describes results of various sensor
data acquisition components; Section 4 presents the com-
putation and communication capabilities of the smart-
phone by observing ECG signal processing results and
experimental challenges using multiple sensor connec-
tions; and finally, Section 5 summarizes the findings and
comments on the future trends of a smart mobile sensing
2. System Design
2.1. Sensors and Communication Link
We have used an eZ430-RF2560 module [19] to demon-
strate the design flow and architecture where a sensor
module requires a processing unit, wireless communica-
tions and low-power consumptions. This particular sys-
tem consists of a MSP430BT5190 microcontroller [20]
and a CC2560 Bluetooth transceiver [21]. Also, it is
equipped with on-board sensors such as a CMA3000-
D01 accelerometer [22] and a TMP106YZC temperature
sensor [23]. The sensor data can be transferred to the
smartphone using the Bluetooth Serial Port Profile (SPP)
protocol. In addition, external sensor data such as ECG
and phonocardiography (PCG) signals can be interfaced.
These peripheral components allow the sensor data to be
mobilized, and the device to operate in low power. Table
1 presents the estimated power consumption of the sen-
sor module where estimation is calculated with common
2 AAA alkaline LR03 batteries for the power supply. The
result shows that it meets the requirement for a portable
sensor module.
An Android smartphone in our system communicates
with the sensor node through Bluetooth SPP protocol to
receive the sensor data in real-time. On the sensor mod-
ule, a UART connection between the sensor and micro-
controller is set at the baud rate of 115, 200 bps, achiev-
ing approximately 15 KB/s of data transfer rate. This is
sufficient enough to provide real-time sensor data acqui-
sition and can be fully maintained under the Bluetooth
SPP protocol. A typical Android smartphone equipped
with two possible options to connect to the Internet: cel-
lular data networks and a built-in Wi-Fi adapter. Both
data paths are suitable to provide the necessary band-
width to achieve real-time sensor data communication
between the smartphone and the central server.
In addition, a typical Bluetooth connection requires a
pairing process prior to establishing data connections by
manually selecting the target Bluetooth device. This ma-
nual procedure can be eliminated by using an NFC tag
Table 1. Estimated sensor system power consumption.
Power Consumption
Maximum (Active) Minimum (Idle)
Microcontroller 0.88 mA - 1.84 mA 1.2 µA - 2.1 µA
Bluetooth Transceiver39.2 mA 40 µA
Accelerometer 70 µA - 90 µA unknown
Temperature Sensor 100 µA 50 µ - 85 µA
Typical ECG Module180 µA unknown
Total 40.43 mA - 41.41 mA 91.2 µA - 127.1 µA
Operational Time using
2 AAA Batteries 1 day 327 - 456 days
Copyright © 2013 SciRes. JST
W.-J. YI, J. SANIIE 49
which provides the MAC address of the targeted Blue-
tooth device for automatic and instantaneous connection
with an application launch. In our design, the NFC tag is
programmed to hold the Bluetooth MAC address of the
sensor module. NFC tag information can be read using
an embedded NFC reader on the Android smartphone by
bringing the smartphone into close proximity of the tag
as shown in Figure 2. Reading different NFC tags using
the smartphone makes it possible to store the target infor-
mation of multiple devices on the Android application.
This capability allows multiple Bluetooth connections for
collecting multiple sensor data. The design procedure of
the multiple Bluetooth connections in our system is in-
troduced in Section 4.
Both automatic and manual Bluetooth connections
must be available on the Android smartphone in case
there is no NFC reader. A comparison of a Bluetooth con-
nection using the NFC and manual Bluetooth connection
is described in Figure 3 where the manual Bluetooth
connection needs tedious steps to initiate the connection,
Figure 2. NFC tagging to initiate the Android application.
Figure 3. Android application design flow of the smart mo-
bile sensing system.
bringing more and unnecessary complexity to users. Our
developed system is capable of using both connection
methods for any Android device including non-NFC
equipped smartphones. After establishing the Bluetooth
SPP connection, the Android application flow entails data
acquisitions, interpretations, displays and data transmis-
sion to central server. The application software receives
and interprets sensor data to display them on the smart-
phone. Then, these data are transferred to the central
server via the Internet connection.
2.2. Intelligent Personal Communication Node
The Intelligent Personal Communication Node (iPCN) is
a system based on an Android smartphone that governs
sensor data including signal processing and data trans-
mission as shown in Figure 4. Also, a limited data proc-
essing at the sensor node level can be achieved using
embedded processor (i.e., smart sensor) which helps to
reduce the computational load on iPCN.
In practice, signal processing can be achieved on the
smartphone without significant hampering of the operat-
ing system and utilization of computational resources.
For example, the QRS detection [24] of the ECG signal
can be achieved in real-time under the Android operating
system environment. The QRS complex, which consists
of Q, R and S waves of the ECG signal, represents right
and left ventricular depolarization of the heart and is
useful to determine heart abnormalities [25]. Also, this
processing is essential to determine the cardiac cycle by
measuring RR intervals (intervals between two R peaks
of the ECG signals) within the QRS complex [25]. The
QRS detection requires band-pass filtering technique
along with adaptive threshold adjustments as shown in
Figure 5. A real-time QRS detection algorithm using a
Figure 4. Smart mobile sensing system architecture.
Copyright © 2013 SciRes. JST
Figure 5. QRS detection algorithm flow.
moving average filter [24] under the Android smartphone
is tested in this study and it will be discussed in Section
One of the main goals for the iPCN application is to
relay the received sensor data to the server and display
them in readable formats. In order to collect and stream
various sensor data correctly, individual data interpreta-
tion methods must be determined. In this section, three
different types of sensor data interpretation are intro-
The on-board accelerometer on the eZ430-RF2560
produces 3 axes values, having an 8-bit data for each axis.
Combined x, y and z-axis data are considered as one data
set. The Android smartphone application processes re-
trieved data sets to display and send to the central server
as described in Algorithm 1. Data is either drawn in
graph form or printed in numerical values for users to
observe their current sensor data stream.
Algorithm 1: Accelerometer Data Application
1: buffer = received accelerometer data
2: While acknowledges all data sets do
3: x-axis = buffer [1st byte]
4: y-axis = buffer [2nd byte]
5: z-axis = buffer [3rd byte]
6: Draw graph (x, y, z) or print text (x, y, z)
7: Send data to server (x, y, z)
8: End While
Similar to the above method, retrieved temperature
sensor data from the eZ430-RF2560 is also processed on
the iPCN where each temperature sensor data is repre-
sented in 8-bits. Similar to the accelerometer data com-
munication, the temperature sensor always sends current
temperature data including consecutive redundant tem-
perature data. Data redundancy must be taken care of for
this particular sensor type on the iPCN application. The
application compares incoming temperature sensor data
with the previous temperature sensor data to determine
consecutive data redundancy as shown in Algorithm 2.
Only non-redundant data are used to display current
temperature data in text on the iPCN and to be sent to the
central server. However, all received data are used for
drawing graph on the smartphone. In this particular ex-
ample, a warning message is generated for user’s con-
venience when the received temperature sensor data
reaches a user-defined value. This concept entails the abi-
lity of creating a smart warning system and can be of
great importance in certain applications such as medical
healthcare monitoring.
Algorithm 2: Temperature Sensor Data Application
1: buffer = received temperature data
2: While acknowledges all data sets do
3: temperature = buffer [byte]
4: If (no temperature changes) then
5: Draw graph (temperature)
6: Else If (temperature changed) then
7: temperature = new temperature
8: Draw graph (temperature) or print text (temperature)
9: If (temperature > = 35) // user-defined range
10: Generate “Warning” Popup // alert user
11: End If
12: Send data to server (temperature)
13: End if
14: End While
For further system evaluation and to demonstrate data
transmission feasibility, ECG and PCG signals at a sam-
pling rate of 250 Hz [26] are used. For this type of sensor
data, all data are scaled from floating point format to
32-bit integers to make it suitable for byte-sized Blue-
tooth SPP data transmissions. Thus, the application needs
to convert and scale back the received data to their origi-
nal format as shown in Algorithm 3. Then, similar to the
Algorithm 1 and Algorithm 2, the smartphone sends the
data to the graph application and to the server through
the Internet.
Algorithm 3: ECG/PCG Sensor Data Application
1: buffer = received ECG/PCG data
2: While acknowledges 4 bytes of data do
3: data = (buffer [1st byte] & 0 xff ) << 24
4: data + = (buffer [2nd byte] & 0 xff) << 16
5: data + = (buffer [3rd byte] & 0 xff) << 8
6: data + = (buffer [4th byte] & 0 xff)
7: Convert (data to original format)
8: Draw graph (data)
9: Send to server (data)
10: End While
Copyright © 2013 SciRes. JST
W.-J. YI, J. SANIIE 51
3. Sensor Data Acquisition
Our customized Android software applications are veri-
fied under various Android operating systems from ver-
sions 2.3 to 4.2 for data acquisitions and transmissions in
real-time using accelerometer data, temperature data, and
ECG and PCG signals. An Android smartphone with an
embedded NFC reader is required to execute automatic
application launch and the automatic Bluetooth connec-
tion. For non-NFC equipped Android devices, the user
needs to launch the application and select the target sen-
sor node manually to start data acquisitions, processing,
display and transmissions.
3.1. Accelerometer Data
Accelerometer data acquisition is achieved by the on-
board accelerometer on the eZ430-RF2560, which is
connected via the UART serial connection to the MSP-
430BT5190 microcontroller. Then, the microcontroller
transmits the processed data in real-time to the smart-
phone through the CC2560 Bluetooth transceiver [21].
Figure 6(a) is the result of acquired accelerometer data
Figure 6. Received real-time accelerometer data display on
the smartphone in (a) text; (b) graph.
in text along with the four last digit of the Bluetooth
MAC address on the sensor node. Figure 6(b) is the re-
sult of acquired accelerometer data in graph from a gen-
tly swiveled accelerometer. Figure 7 is the acquired ac-
celerometer data on the server simultaneously from the
smartphone via the Internet connection.
Many studies are explored for movement detections
using the accelerometer data analysis. However, they all
share the same idea for the analysis; their systems com-
pare the incoming data with pre-recorded templates in
real-time [4,5,12,27]. To improve accuracy, numerous
attempts must be achieved to set a template for a desired
detection movement. Multiple accelerometer data are
preferred for sophisticated movement detections [28],
including posture and movement transition detections.
In this study, we have explored a simple accelerometer
data analysis using the data retrieved on the smartphone.
The x-axis interpretation can be accomplished when the
absolute value of x-axis data is greater than the absolute
value of y-axis. Depending on the orientation of the de-
vice, x-axis data between 60 and 50 represents a
steady LEFT, whereas between +50 and +60 represents a
steady RIGHT. For the other two directions, similar rules
apply to detect the direction observing y-axis data. Fig-
ure 8 shows a simple accelerometer analysis of the raw
data interpretation, detecting four two-dimensional direc-
We have extended this accelerometer data interpretation
Figure 7. Received real-time accelerometer data on the ser-
Figure 8. Two-dimensional accelerometer movement detec-
Copyright © 2013 SciRes. JST
to the Snake Game sample from Android Developers
[29]. The original game requires touch gestures on the
screen to play the game. The touch screen inputs are re-
placed by the accelerometer using the above method to
interpret the raw data. Figure 9 shows the snake’s move-
ment by using the accelerometer on the microcontroller.
This particular experiment emphasizes that the acceler-
ometer could be used for subject movement detections
using 3 axes data. Also, multiple accelerometer data in-
tegration for sophisticated movement detections will in-
crease the movement estimation accuracy.
3.2. Temperature Sensor Data
The temperature sensor on the eZ430-RF2560, detecting
the room temperature in real-time is used for the data
acquisition. Similar to the previous method, the acquired
data can be displayed in text or graph on the smartphone
screen. A heat gun is used to demonstrate room tempera-
ture changes on the embedded temperature sensor as
shown in Figure 10.
Figure 11 is the result of the temperature sensor data
acquisition on the smartphone in text and graph. In par-
ticular, as shown in Figure 11(b), a system warning po-
pup message is implemented when the temperature goes
beyond 35 degrees Celsius to alert the user on the graph
application. This application also sends data to the cen-
tral server simultaneously for data storage and analysis,
as shown in Figure 12. As described in the previous sec-
Figure 9. Remote controlling Snake Game.
Figure 10. Testing temperature sensor data transmission.
Figure 11. Received real-time temperature data display on
the smartphone in (a) text; (b) grap.
tion, consecutive redundant temperature data are only
considered for the graph application, but discarded when
the data is represented in text.
3.3. ECG and PCG Data
Pre-recorded ECG and PCG signal data from an online
database [26] are used to demonstrate the feasibility of
Copyright © 2013 SciRes. JST
W.-J. YI, J. SANIIE 53
simultaneous sensor data acquisition and display on the
smartphone, and stream to a central server. For example,
the ECG and PCG data are sent to the smartphone using
Bluetooth SPP protocol, and plotted for display (see Fig-
ure 13) and simultaneously transmitted to a central ser-
ver through the Internet connection.
4. Smartphone Computation and
4.1. Computation Evaluation
To evaluate the computational capability of the smart-
phone for signal processing, we have implemented fast
Fourier transform (FFT) of the received ECG data on the
Android smartphone as shown in Figure 14.
We have tested two different types of Android smart
Figure 12. Received real-time temperature data on the ser-
Figure 13. Received real-time sensor data on the smart-
phone of (a) ECG signal data; (b) PCG signal data.
phones, Nexus S and Galaxy S III, where the Nexus S
represents the low-end smartphone and the Galaxy S III
represents relatively most up-to-date high-end smart-
phone, in terms of CPU power. As shown in Table 2,
both Android smartphones’ performance is satisfactory
and practically can handle FFT computations in the order
of tens of milliseconds when the data size is a few thou-
sand samples. This result proves that most of the com-
mon Android smartphones available in the market have
sufficient computing power to accomplish signal proc-
4.2. QRS Detection and Heart Beat Rate (HBR)
ECG signal analysis is useful to determine the heart ac-
tivity which can be analyzed by observing the ECG sig-
nal characteristics in real-time. In our study, the HBR is
calculated by identifying RR intervals based on the result
obtained from QRS peak detections using a moving av-
erage filter [24] on the Android smartphone. This method
can determine the HBR in real-time and does not need to
wait for 60 seconds to calculate the heart beats per min-
ute (BPM) since every measurement of RR intervals can
be converted into the HBR. Figure 15 shows the HBR in
BPM alongside with the display of ECG signal in real-
Figure 14. ECG signal and FFT result displayed on the
Table 2. Smartphone and FFT computation performance
Computation Time
N Samples for
FFT Nexus S Android
Galaxy S III Android
128 10.27 ms 0.26 ms
256 17.40 ms 1.89 ms
512 34.10 ms 6.64 ms
1024 44.10 ms 12.59 ms
2048 65.86 ms 21.79 ms
Copyright © 2013 SciRes. JST
Figure 15. Received real-time ECG signal data and HBR in
4.3. Multi-Sensor Connections
According to the Bluetooth protocol specification, multi-
ple concurrent connections up to 7 slaves to 1 master
node are possible [30]. This can be applied to multiple
sensor data transfers without interrupting other sensor
data acquisition processes. However, this configuration
produces unstable smartphone system operations. To
overcome this issue, our design avoids concurrent data
transmissions between the smartphone and the sensor
nodes, but connects to different sensors and commits
sensor data transfer instantly when an interrupt is trig-
gered on that particular sensor.
In order to connect to different sensor nodes, the in-
formation of the sensor nodes must be registered to the
Android application. This can be achieved by reading
multiple Bluetooth MAC addresses using NFC tags. As
the NFC and the Bluetooth can be used concurrently on
the Android system, the new sensor node information can
be obtained at any time even during Bluetooth data
transmissions. When registering new sensor node infor-
mation to the Android application, it only connects to the
sensor node and awaits for the data transfer requests. By
this arrangement, less system resources of the Android
operating system is required for the operation. Figure 16
is the design flow of multiple sensor node connection.
The application is designed to remove NFC tag infor-
mation if a repeat tag action by the same sensor node is
recognized. This action will help to avoid repeatedly reg-
istering the same sensor node. However, this does not
apply when there is only one MAC address registered on
the queue. Furthermore, the removed MAC address in-
formation can be restored by reading the NFC tag. The
application provides the information to the user about the
connected devices in real-time. As shown in Figure 17,
two different accelerometers are used to demonstrate
connection transitions from one node to the other. Dif-
Figure 16. Android application flow of multiple sensor con-
ferentiating the types of the sensor data can be accom-
plished by identifying the Bluetooth MAC address in-
formation on the sensor or by programming the informa-
tion on to the NFC tag. Figure 18 is the result displayed
on the server where the sensor data type can be differen-
tiated by determining the ID field which is the last four
digits of the Bluetooth MAC address.
5. Conclusion
In this paper, the system architecture and design flow of a
smart mobile sensing system using an Android smart-
phone are introduced. The smartphone in our design is
able to achieve sensor data acquisition and transmission
using multiple wireless protocols in real-time as well as
data display. The sensor node is desired to be a low-
power and efficient system to improve user-experience,
Copyright © 2013 SciRes. JST
W.-J. YI, J. SANIIE 55
(a) (b)
Figure 17. Received real-time accelerometer data on the
smartphone during transitions (a) from Device 7A:32 to
7A:40; (b) from Device 7A:40 to 7A:32.
Figure 18. Received real-time accelerometer data on the
server during transitions from Device 7A:40 to 7A:32.
and to deliver critical information on-demand. Various
sensor data including accelerometer, temperature sensor,
ECG and PCG data are used to demonstrate the feasibil-
ity of the system for real-time sensor data transmission
and processing. FFT computation and QRS detection are
tested on the Android smartphone for real-time signal
processing. This system design has improved the effi-
ciency for user interactions by combining NFC technol-
ogy with the Bluetooth protocol. This arrangement elim-
inates monotonous Bluetooth pairing and connection
procedures, and enables automatic executions of the An-
droid application. In addition, multiple sensor node con-
nections via the Bluetooth are possible using the NFC by
registering multiple Bluetooth device information on the
Android application. The developed system has the ca-
pability to be further extended not only to day-to-day
patient health status monitoring, but also to subjects
working in hazardous environment that require continu-
ous monitoring. Any complex systems demanding multi-
ple sensors for remote monitoring safe operations and/or
integrity of multipart structures used in transportation
systems, civil structures or equipment are also applicable
for this design.
[1] A. Alexander, “Smartphone Usage Statistics 2012,” 2012.
[2] T. Mogg, “US Smartphone Users Now over 100 Million,
Android Increases Market Share,” 2012.
[3] I. Korhonon, J. Parkka and M. Van Gils, “Health Moni-
toring in the Home of the Future,” IEEE Engineering in
Medicine and Biology Magazine, Vol. 22, No. 3, 2003, pp.
66-73. doi:10.1109/MEMB.2003.1213628
[4] H. Tanaka, R. Kimura and S. Ioroi, “Equipment Opera-
tion by Motion Recognition with Wearable Wireless Ac-
celeration Sensor,” Third International Conference on
Next Generation Mobile Applications, Services and
Technologies (NGMAST ’09), Cardiff, 15-19 September
2009, pp. 114-118. doi:10.1109/NGMAST.2009.96
[5] C. J. James and S. Kumar, “Detection of Posture and
Motion by Accelerometer Sensors,” 3rd International
Conference on Electronics Comp uter Technol ogy (ICECT),
Kanyakumari, 8-10 April 2011, pp. 369-371.
[6] P.K. Baheti and H. Garudadri, “An Ultra Low Power
Pulse Oximeter Sensor Based on Compressed Sensing,”
6th International Workshop on Wearable and Implant-
able Body Sensor Networks, Berkley, 3-5 June 2007, pp.
[7] M.-H. Cheng, et al., “A Vital Wearing System with Wire-
less Capability,” 2nd International Conference on Perva-
sive Computing Technologies for Healthcare, Tampere,
30 January-1 February 2008, pp. 268-271.
[8] H. Ghasemzadeh, V. Loseu and R. Jafari, “Structural
Action Recognition in Body Sensor Networks: Distrib-
uted Classification Based on String Matching,” IEEE
Transactions on Information Technology in Biomedicine,
Vol. 14, No. 2, 2010, pp. 425-435.
[9] S.-L. Chen, H.-Y. Lee, C.-A. Chen, H.-Y. Huang and
C.-H. Luo, “Wireless Body Sensor Network with Adap-
tive Low-Power Design for Biometrics and Healthcare
Applications,” IEEE Systems Journal, Vol. 3, No. 4, 2009,
pp. 398-409. doi:10.1109/JSYST.2009.2032440
[10] J. Yoo, L. Yan, S. Lee, Y. Kim and H.-J. Yoo, “A 5.2mW
Self-Configured Wearable Body Sensor Network Con-
troller and a 12uW Wirelessly Powered Sensor for a Con-
tinuous Health Monitoring System,” IEEE Journal of
Solid-State Circuits, Vol. 45, No. 1, 2010, pp. 178-188.
[11] H. Li and J. Tan, “An Ultra-Low-Power Medium Access
Control Protocol for Body Sensor Network,” IEEE 62nd
Vehicular Technology Conference, Dallas, 25-28 Sep-
tember 2005, pp. 2342-2346.
[12] B. P. L. Lo and G. Yang, “Key Technical Challenges and
Current Implementations of Body Sensor Networks,” 2nd
International Workshop on Wearable and Implantable
Body Sensor Networks, London, 12-13 April 2005.
Copyright © 2013 SciRes. JST
Copyright © 2013 SciRes. JST
[13] S. Hendershot, A. Hilton and M. Oo, “Wireless Wearable
Motion Sensor for Use in Medical Care,” IEEE Published
Student Application Papers, 2009.
[14] N. Zarka, M. F. Hinnawi, A. Dardari and M. A. Tayyan,
“Patient Keeper” Medical Application on Mobile Phone,”
Information and Communication Technologies: From
Theory to Applications, Damascus, 19-23 April 2004, pp.
[15] C. Rotariu, H. Constin, G. Andruseac, R. Ciobotariu and
F. Adochiei, “An Integrated System for Wireless Moni-
toring of Chronic Patients and Elderly People,” 15th In-
ternational Conference on System Theory, Control and
Computing (ICSTCC), Sinaia, 14-16 October 2011, pp.
[16] C. A. Otto, E. Jovanov and A. Milenkovic, “A WBAN-
Based System for Health Monitoring at Home,” 3rd
IEEE/EMBS International Summer School on Medical
Devices and Biosensors , Boston, 4-6 September 2006, pp.
[17] N. Bricon-Souf, D. Delerue, H. Bezzazi, D. Donsez and
R. J. Beuscart, “A Regional Server for Medical Informa-
tion,” Proceedings of the 19th Annual International Con-
ference of the IEEE Engineering in Medicine and Biology
Society, Chicago, 30 October-2 November 1997, pp. 939-
[18] W. Yi, W. Jia and J. Saniie, “Mobile Sensor Data Col-
lector using Android Smartphone,” IEEE 55th Interna-
tional Midwest Symposium on Circuits and Systems,
Boise, 5-8 August 2012, pp. 956-959.
[19] Texas Instruments, “Texas Instruments Embedded Proc-
essors Wiki for eZ430-RF2560,” 2013.
[20] Texas Instruments, “MSP430BT5190 Mixed Signal Mi-
crocontroller,” 2010.
[21] Texas Instruments, “CC2560 Bluetooth Single-Chip So-
lution,” 2010.
[22] Murata Electronics, “CMA3000-D01 3-Axis Ultra Low
Power Accelerometer with Digital SPI and I2C Inter-
face,” 2012.
[23] Texas Instruments, “Digital Temperature Sensor with
Two-Wire Interface,” 2006.
[24] H. C. Chen and S. W. Chen, “A Moving Average based
Filtering System with Its Application to Real-Time QRS
Detection,” Computers in Cardiology, Thessaloniki, 21-
24 September 2003, pp. 585-588.
[25] F. G. Yanowitz, “ECG Learning Center—An Introduction
to Clinical Electrocardiography,” 2012.
[26] A. L. Goldberger, et al., “PhysioBank, PhysioToolkit,
and PhysioNet: Components of a New Research Resource
for Complex Physiologic Signals,” Circulation, Vol. 101,
No. 23, 2000, pp. e215-e220.
[27] K. D. Nguyen, I.-M. Chen, Z. Luo, S. H. Yeo and H. B.-L.
Duh, “A Wearable Sensing System for Tracking and
Monitoring of Functional Arm Movement,” IEEE/ASME
Transactions on Mechatronics, Vol. 16, No. 2, 2011, pp.
213-220. doi:10.1109/TMECH.2009.2039222
[28] N. Crampton, K. Fox, H. Johnston and A. Whitehead,
“Dance, Dance Evolution: Accelerometer Sensor Net-
works as Input to Video Games,” IEEE International
Workshop on Haptic, Audio and Visual Environment and
Games, Ottawa, 12-14 October 2007, pp. 107-112.
[29] Android Developers, “Samples,” 2013.
[30] I. H. Mulyadi, E. Supriyanto, N. M. Safri and M. H. Sa-
tria, “Wireless Medical Interface Using ZigBee and Blue-
tooth Technology,” 3rd Asia International Conference on
Modelling & Simulation, Bandung, 25-29 May 2009, pp.