System Architecture and Design Flow of Smart Mobile Sensing Systems


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 capability tests, we have observed the signal processing performance of the smartphone by implementing fast Fourier transform (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 monitoring.


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W. Yi and J. Saniie, "System Architecture and Design Flow of Smart Mobile Sensing Systems," Journal of Sensor Technology, Vol. 3 No. 3, 2013, pp. 47-56. doi: 10.4236/jst.2013.33009.

Conflicts of Interest

The authors declare no conflicts of interest.


[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 Monitoring 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 Operation by Motion Recognition with Wearable Wireless Acceleration 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 Computer Technology (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 Implantable Body Sensor Networks, Berkley, 3-5 June 2007, pp. 144-148.
[7] M.-H. Cheng, et al., “A Vital Wearing System with Wireless Capability,” 2nd International Conference on Pervasive 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: Distributed Classification Based on String Matching,” IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 2, 2010, pp. 425-435. doi:10.1109/TITB.2009.2036722
[9] S.-L. Chen, H.-Y. Lee, C.-A. Chen, H.-Y. Huang and C.-H. Luo, “Wireless Body Sensor Network with Adaptive 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 Controller and a 12uW Wirelessly Powered Sensor for a Continuous Health Monitoring System,” IEEE Journal of Solid-State Circuits, Vol. 45, No. 1, 2010, pp. 178-188. doi:10.1109/ISSCC.2009.4977422
[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 September 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. Body_Sensor_Networks.pdf
[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. 37-38.
[15] C. Rotariu, H. Constin, G. Andruseac, R. Ciobotariu and F. Adochiei, “An Integrated System for Wireless Monitoring of Chronic Patients and Elderly People,” 15th International Conference on System Theory, Control and Computing (ICSTCC), Sinaia, 14-16 October 2011, pp. 1-4.
[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. 20-23.
[17] N. Bricon-Souf, D. Delerue, H. Bezzazi, D. Donsez and R. J. Beuscart, “A Regional Server for Medical Information,” Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, 30 October-2 November 1997, pp. 939- 940.
[18] W. Yi, W. Jia and J. Saniie, “Mobile Sensor Data Collector using Android Smartphone,” IEEE 55th International Midwest Symposium on Circuits and Systems, Boise, 5-8 August 2012, pp. 956-959.
[19] Texas Instruments, “Texas Instruments Embedded Processors Wiki for eZ430-RF2560,” 2013.
[20] Texas Instruments, “MSP430BT5190 Mixed Signal Microcontroller,” 2010.
[21] Texas Instruments, “CC2560 Bluetooth Single-Chip Solution,” 2010.
[22] Murata Electronics, “CMA3000-D01 3-Axis Ultra Low Power Accelerometer with Digital SPI and I2C Interface,” 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. doi:10.1161/01.CIR.101.23.e215
[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 Networks as Input to Video Games,” IEEE International Workshop on Haptic, Audio and Visual Environment and Games, Ottawa, 12-14 October 2007, pp. 107-112. doi:10.1109/HAVE.2007.4371597
[29] Android Developers, “Samples,” 2013.
[30] I. H. Mulyadi, E. Supriyanto, N. M. Safri and M. H. Satria, “Wireless Medical Interface Using ZigBee and Blue-tooth Technology,” 3rd Asia International Conference on Modelling & Simulation, Bandung, 25-29 May 2009, pp. 276-281.

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