Exploiting LabVIEW FPGA in Implementation of Real-Time Sensor Data Acquisition for Rowing Monitoring System

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


Field Programmable Gate Arrays (FPGAs) platform has been increasingly used in sensor-based applications because of reconfigurable and parallelisms features offered in the FPGA. However, most of the application designers are unfamiliar with hardware programming and design concepts of the FPGA. This paper presents an implementation of real-time sensor data acquisition (ReSDAq) for rowing monitoring system using LabVIEW FPGA which utilising the high-level synthesis (HLS) technique. The HLS allows application designers to use high-level language for configuring the FPGA. The ReSDAq application comprises of a tri-axis accelerometer sensor, an LCD monitor, and a National Instrument (NI) sbRIO-9632 board. The sbRIO-9632 board was targeted programmed on the Xilinx FPGA core to acquire sensor data and compute acceleration of the arm movement of the rower. From this study, it was found that the compilation time to convert G-code into hardware description language (HDL) code depends on the size of the code. Apart from having an interesting experience in graphical programming approach, the LabVIEW FPGA module could be used by application designers to facilitate and accelerate the development of FPGA-based systems.


LabVIEW FPGA Rowing Data acquisition Sensor 



The authors would like to thank the Ministry of Higher Education, Malaysia and Universiti Tun Hussein Onn Malaysia (UTHM) for funding this study.


  1. 1.
    Sforza, C., Casiraghi, E., Lovecchio, N., Galante, D., Ferrario, V.F.: A three-dimensional study of body motion during Ergometer rowing. Open Sports Med. J. 1(6), 22–28 (2012)CrossRefGoogle Scholar
  2. 2.
    Bernstein, I.A., Webber, O., Woledge, R.: An ergonomic comparison of rowing machine designs: possible implications for safety. Br. J. Sports Med. 36, 108–112 (2002)CrossRefGoogle Scholar
  3. 3.
    Shi, G., He, Y., Ye, F., Yang, J., Wang, P., Jin, Y.: Towards an ubiquitous motion capture system using inertial MEMS sensors and ZigBee network. In: International Conference on Cyber Tech. in Automation, Control, and Intelligent System, pp. 230–234. IEEE, Kunming (2011)Google Scholar
  4. 4.
    Borghetti, M., Sardini, E., Serpelloni, M.: Evaluation of bend sensors for limb motion monitoring. In: International Symposium on Medical Measurements and Applications, pp. 1–5. IEEE, Lisboa (2014)Google Scholar
  5. 5.
    Byrd, G.: 21st Century Pong. Computer 48(10), 80–84 (2015)CrossRefGoogle Scholar
  6. 6.
    Valeria, R., Stefan, L., Vesa, L., Yves, V., Walter, R., Laura, G.: Trunk kinematics during cross country Sit-skiing Ergometry: Skiing strategies associated to neuromusculoskeletal impairment. In: International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE, Benevento (2016)Google Scholar
  7. 7.
    Taha, Z., Hassan, M.S.S., Yap, H.J., Yeo, W.K.: Preliminary investigation of an innovative digital motion analysis device for badminton athlete performance evaluation. In: 11th Conference of the International Sports English Association. Procedia Engineering 147, 461–465 (2016)Google Scholar
  8. 8.
  9. 9.
    Zhu, R., Zhaoying, Z.: A real-time articulated human motion tracking using tri-axis inertial/magnetic sensors package. IEEE Trans. Neural Syst. Rehabil. Eng. 12(2), 295–302 (2004)CrossRefGoogle Scholar
  10. 10.
    King, R.C., McIlwraith, D.G., Lo, B., Pansiot, J., McGregor, A.H., Yang, G.Z.: Body sensor networks for monitoring rowing technique. In: 2009 Proceedings on 6th International Workshop on Wearable and Implantable Body Sensor Networks, pp. 251–255. IEEE, Berkeley (2009)Google Scholar
  11. 11.
    Yurish, S.Y.: High Performance Digital Sensors Design: How to Make It Smarter,
  12. 12.
    De La Piedra, A., Braeken, A., Touhafi, A.: Sensor systems based on FPGAs and their applications: a survey. Sensors 12(9), 12235–12264 (2012)CrossRefGoogle Scholar
  13. 13.
    Minouni, E.H.E., Karim, M., Kouache, M.E., Amarouch, M.Y.: An FPGA-based system for real-time electrocardiographic detection of STEMI. In: 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP, pp. 830–835. IEEE, Monastir) (2016)Google Scholar
  14. 14.
    Oballe-Peinado, Ó., Vidal-Verdú, F., Sánchez-Durán, J.A., Castellanos-Ramos, J., Hidalgo-López, J.A.: Smart capture modules for direct sensor-to-FPGA interfaces. Sensors. 15(12), 31762–31780 (Dec 16, 2015)Google Scholar
  15. 15.
    García, G.J., Jara, C.A., Pomares, J., Alabdo, A., Poggi, L.M., Torres, F.: A Survey on FPGA-based sensor systems: towards intelligent and reconfigurable low-power sensors for computer vision. Control Signal Process. Sensors 14, 6247–6278 (2014)Google Scholar
  16. 16.
    Ponce-Cruz, P., Molina, A., MacCleery, B.: LabVIEWTM FPGA. In: Fuzzy Logic Type 1 and Type 2 Based on LabVIEW™ FPGA. Series Studies in Fuzziness and Soft Computing. vol. 334, pp 71–138. Springer International Publishing, Switzerland (2016)Google Scholar
  17. 17.
    Andrade, H.A., Ahrends, S., Hogg, S.: Making FPGAs Accessible with LabVIEW. In: Koch, D., Hanning, F., Ziener, D. (eds.) FPGAs for Software Programmers, pp. 63–79. Springer International Publishing, Switzerland (2016)CrossRefGoogle Scholar
  18. 18.
    Wang, G., Tran, T.N., Andrade, H.A.: A graphical programming and design environment for FPGA-based hardware. In: 2010 International Conference on Field Programmable Technology, pp. 337–340. IEEE, Beijing (2010)Google Scholar
  19. 19.
    Nane, R., Sima, V.M., Pilato, C., Choi, J., Fort, B., Canis, A., Chen, Y.T., Hsiao, H., Brown, S., Ferrandi, F., Anderson, J., Bertels, K.: A Survey and evaluation of FPGA high-level synthesis tools. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 35(10), 1591–1604 (2016)CrossRefGoogle Scholar
  20. 20.
    Fuller, D.: The Future of FPGA Design Software, Jan. 24, 2013,
  21. 21.
    Accelerometer ADXL335 Datasheet, 2009–2010 Analog Devices.
  22. 22.
    NI sbRIO-961x/963x/964x and NI sbRIO-9612XT/9632XT/9642XT National Instruments,
  23. 23.

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Computer Engineering, Faculty of Electrical & Electronic EngineeringUniversiti Tun Hussein Onn MalaysiaBatu PahatMalaysia
  2. 2.Reconfigurable Computing for Analytics Acceleration (ReCAA) Research Laboratory, Microelectronics and Nanotechnology—Shamsuddin Research Centre (MiNT-SRC)Universiti Tun Hussein Onn MalaysiaBatu PahatMalaysia

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