volume | PIER Journals

Abstract

In the development of hand gesture based Human to Machine Interface, the Doppler response feature extraction method plays an important role in translating hand gesture of certain information. The Doppler response feature extraction method from hand gesture sign was proposed and designed by combining time and frequency domain analysis. The extraction of the Doppler response features at the time domain is developed by using cross correlation, and the time domain feature is represented by using peak value of cross correlation result and its time shift. The Doppler response feature of frequency domain is extracted by employing a discriminator filter determined by the frequency spectrum observation of Doppler response. The proposed method was employed as a pre-processing for Continuous Wave (CW) radar output signals, which is able to relieve the pattern classification of Doppler response associated with each hand gesture. The simulation and laboratory experiment using HB 100 Doppler radar were performed to investigate the proposed method. The results show that the combination of all three features was capable of differentiating every type of hand gestures movement.

1. Harris, N., "The design and development of assistive technology," IEEE Potentials, Vol. 36, No. 1, 24-28, 2017, doi: 10.1109/MPOT.2016.2615107.
doi:10.1109/MPOT.2016.2615107

2. Calder, D. J., "Assistive technology interfaces for the blind," Proceeding of 3rd IEEE International Conference on Digital Ecosystems and Technologies, 318-323, Istanbul, June 2009, doi: 10.1109/DEST.2009.5276752.

3. Zheng, X., X. Li, J. Liu, W. Chen, and Y. Hao, "A portable wireless eye movement-controlled Human-Computer Interface for the disabled," Proceeding of 2009 ICME International Conference on Complex Medical Engineering, 1-5, Tempe, AZ, April 9-11, 2009, doi: 10.1109/ICCME.2009.4906647.

4. Parmar, K., B. Mehta, and R. Sawant, "Facial-feature based Human-Computer Interface for disabled people," Proceeding of 2012 International Conference on Communication, Information and Computing Technology (ICCICT), 1-5, Mumbai, October 19-20, 2012, doi: 10.1109/ICCICT.2012.6398171.

5. Berjn, R., et al. "Alternative Human-Machine Interface system for powered wheelchairs," Proceeding of 2011 IEEE 1st International Conference on Serious Games and Applications for Health (SeGAH), Vol. 1, 1-5, Braga, November 16-18, 2011, doi: 10.1109/SeGAH.2011.6165452.

6. Panwar, M., "Hand gesture recognition based on shape parameters," Proceeding of 2012 International Conference on Computing, Communication and Applications, India, February 22-24, 2012.

7. Jin, X., S. Sarkar, A. Ray, S. Gupta, and T. Damarla, "Target detection and classification using seismic and PIR sensors," IEEE Sensors Journal, Vol. 12, No. 6, 1709-1718, 2012, doi: 10.1109/JSEN.2011.2177257.
doi:10.1109/JSEN.2011.2177257

8. Gaba, N., N. Barak, and S. Aggarwal, "Motion detection, tracking and classification for automated Video Surveillance," Proceeding of 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, July 4-6, 2016, doi: 10.1109/ICPEICES.2016.7853536.

9. Lu, X., C. C. Chen, and J. K. Aggarwal, "Human detection using depth information by Kinect," Proceeding of 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Springs, Colorado, USA, June 20-25, 2011.

10. Oh, C. M., et al. "Upper body gesture recognition for human-robot interaction," Human-Computer Interaction, Interaction Techniques and Environments. Lecture Notes in Computer Science, 294303, Springer, Berlin, Heidelberg, 2011.

11. Wei, T., Y. Qiao, and B. Lee, "Kinect skeleton coordinate calibration for remote physical training," MMEDIA 2014: The Sixth International Conferences on Advances in Multimedia, Nice, France, February 23-27, 2014.

12. Nishida, Y., "Proximity motion detection using 802.11 for mobile devices," Proceeding of 2007 IEEE International Conference on Portable Information Devices, Orlando, May 25-29, 2007, doi: 10.1109/PORTABLE.2007.7.

13. Guo, L., L. Wang, J. Liu, and W. Zhou, "A survey on motion detection using WiFi signals," Proceeding of 2016 12th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN), Vol. 1, 202-206, Hefei, December 18-19, 2016, doi: 10.1109/MSN.2016.040.

14. Zhang, D., et al. "FMCW radar for small displacement detection of vital sign using projection matric method," International Journal of Antenna and Propagation, 1-5, 2013, doi: 10.1155/2013/571986.

15. Wang, Y., et al. "Detecting and monitoring the micro-motions of trapped people hidden by obstacles based on wavelet entropy with low centre-frequency UWB radar ," International Journal of Remote Sensing, Vol. 36, No. 5, 1349-1366, 2015, 10.1080/01431161.2015.1009651.
doi:10.1080/01431161.2015.1009651

16. Ambarini, R., et al. "Single-tone Doppler radar system for human respiratory monitoring," 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), Malang, Indonesia, October 16-18, 2018, doi: 10.1109/EECSI.2018.8752871.

17. Li, C., et al. "A noncontact FMCW radar for dispalcement measurement in structure health monitoring," Sensor, Vol. 15, 7412-7433, 2015.
doi:10.3390/s150407412

18. De Macedo, K. A. C., "A compact ground-based interferometric radar for landslide monitoring: The Xerém experiment," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 10, No. 3, 975-986, 2017.
doi:10.1109/JSTARS.2016.2640316

19. Mahafza, B. R., Radar System Analysis and Design, CRC Press, 2013.

20. Nuti, P., E. Yavari, and O. Boric-Lubecke, "Doppler radar occupancy sensor for small-range motion detection," Proceeding of IEEE Asia Pacific Microwave Conference (APMC), Kuala Lumpar, November 13-16, 2017, doi: 10.1109/APMC.2017.8251411.

21. Gu, C., Z. Peng, and C. Li, "High-precision motion detection using low-complexity doppler radar with digital post-distortion technique," IEEE Transactions on Microwave Theory and Techniques, Vol. 64, No. 3, 961-971, 2016, doi: 10.1109/TMTT.2016.2519881.

22. Zheng, C., et al. "Doppler biosignal detection based time-domain hand gesture recognition," Proceeding of IEEE MTT-S Int. Microw. Workshop Ser. RF Wireless Technol. Biomed. Healthcare Appl. (IMWS-BIO), December 9-11, 2013, doi: 10.1109/IMWS-BIO.2013.6756200.

23. Peng, Z., C. Li, J. Munoz-Ferreras, and R. Gomez-Garcia, "An FMCW radar sensor for human gesture recognition in the presence of multiple targets," Proceeding of 2017 First IEEE MTT-S International Microwave Bio Conference (IMBIOC), Gothenburg, May 15-17, 2017, doi: 10.1109/IMBIOC.2017.7965798.

24. Zhang, J., J. Tao, and Z. Shi, "Doppler-radar based hand gesture recognition system using convolutional neural networks," Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, 463, Springer, 2017.

25. Fan, T., et al. "Wireless hand gesture recognition based on continuous-wave Doppler radar sensors," IEEE Transactions on Microwave Theory and Techniques, Vol. 64, No. 11, 4012-4020, 2016, doi: 10.1109/TMTT.2016.2610427.
doi:10.1109/TMTT.2016.2610427

26. Ryu, S., et al. "Feature-based hand gesture recognition using an FMCW radar and its temporal feature analysis," IEEE Sensors Journal, Vol. 18, No. 18, 7593-7602, 2018, doi: 10.1109/JSEN.2018.2859815.
doi:10.1109/JSEN.2018.2859815

27. Dardas, N. H. and N. D. Georganas, "Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques," IEEE Transactions on Instrumentation and Measurement, Vol. 60, No. 11, 3592-3607, 2011, doi: 10.1109/TIM.2011.2161140.
doi:10.1109/TIM.2011.2161140

28. Lubow, B., "Correlation entering new fields with real-time signal analysis," IEEE Transactions on Electromagnetic Compatibility, EMC, Vol. 10, No. 2, 284-284, 1968, doi: 10.1109/TEMC.1968.302964.
doi:10.1109/TEMC.1968.302964

29. Kim, J. and J. A. Fessler, "Intensity-based image registration using robust correlation coefficients," IEEE Transactions on Medical Imaging, Vol. 23, No. 11, 1430-1444, 2004, doi: 10.1109/TMI.2004.835313.
doi:10.1109/TMI.2004.835313

30. Negi, S., Y. Kumar, and V. M. Mishra, "Feature extraction and classification for EMG signals using linear discriminant analysis," Proceeding of 2016 2nd International Conference on Advances in Computing, Communication, and Automation (ICACCA) (Fall), 1-6, Bareilly, 2016, doi: 10.1109/ICACCAF.2016.7748960.

31. Jahankhani, P., V. Kodogiannis, and K. Revett, "EEG signal classification using wavelet feature extraction and neural networks," IEEE John Vincent Atanasoff 2006 International Symposium on Modern Computing (JVA’06 ), Sofia, December 4-8, 2006, doi: 10.1109/JVA.2006.17.

32. Li, D., W. Pedrycz, and N. J. Pizzi, "Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification," IEEE Transactions on Biomedical Engineering, Vol. 52, No. 6, 1132-1139, 2005, doi: 10.1109/TBME.2005.848377.
doi:10.1109/TBME.2005.848377

Dr. Aloysius Adya Pramudita

Dr. Lukas

Dr. Edwar