DOI QR코드

DOI QR Code

Hand Gesture Recognition Suitable for Wearable Devices using Flexible Epidermal Tactile Sensor Array

  • Received : 2018.01.09
  • Accepted : 2018.03.27
  • Published : 2018.07.01

Abstract

With the explosion of digital devices, interaction technologies between human and devices are required more than ever. Especially, hand gesture recognition is advantageous in that it can be easily used. It is divided into the two groups: the contact sensor and the non-contact sensor. Compared with non-contact gesture recognition, the advantage of contact gesture recognition is that it is able to classify gestures that disappear from the sensor's sight. Also, since there is direct contacted with the user, relatively accurate information can be acquired. Electromyography (EMG) and force-sensitive resistors (FSRs) are the typical methods used for contact gesture recognition based on muscle activities. The sensors, however, are generally too sensitive to environmental disturbances such as electrical noises, electromagnetic signals and so on. In this paper, we propose a novel contact gesture recognition method based on Flexible Epidermal Tactile Sensor Array (FETSA) that is used to measure electrical signals according to movements of the wrist. To recognize gestures using FETSA, we extracted feature sets, and the gestures were subsequently classified using the support vector machine. The performance of the proposed gesture recognition method is very promising in comparison with two previous non-contact and contact gesture recognition studies.

Keywords

References

  1. Chao Xu, Parth, H. Pathak and Prasant Mohapatra, "Finger-writing with Smartwatch: A Case for Finger and Hand Gesture Recognition using Smartwatch". Proceedings of the 16th International Workshop on Mobile Computing Systems and Applications, Santa Fe, NM, USA, Feb. 2015.
  2. Zhiyuan Lu, Xiang Chen, Qiang Li, Xu Zhang, and Ping Zhou, "A Hand Gesture Recognition Framework and Wearable Gesture-Based Interaction Prototype for Mobile Devices" IEEE Transactions on HumanMachine Systems, vol. 44, no. 2, pp. 293-299, April 2014. https://doi.org/10.1109/THMS.2014.2302794
  3. Xiang Chen, Xu Zhang, Zhang-Yan Zhao, Ji-Hai Yang, Vuokko Lantz and Kong-Qiao Wang, "Hand Gesture Recognition Research Based on Surface EMG Sensors and 2D-accelerometers," Wearable Computers, 2007 11th IEEE International Symposium on, Boston, MA, USA, Oct. 2007.
  4. Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Lochtefeld and Antonio Kruger, "EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing," CHI '16 Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, San Jose, CA, USA, May 2007.
  5. Yang Zhang, Chris Harrison, "Tomo: Wearable, LowCost Electrical Impedance Tomography for Hand Gesture Recognition," UIST '15 Proceedings of the 28th Annual ACM Symposium on User Interface Software & Technology, pp. 167-173, Nov. 2015.
  6. Zhihan, Alaa Halawani, Shengzhong Feng, Shafiq ur Réhman and Haibo Li, "Touch-less interactive augmented reality game on vision-based wearable device," Abbrev. Personal and Ubiquitous Computing, vol. 19, no. 3-4, pp. 551-567, July 2015. https://doi.org/10.1007/s00779-015-0844-1
  7. Youngwook Kim, Brian Toomajian, "Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network," IEEE Access, vol. 4, pp. 7125-7130, Oct. 2016. https://doi.org/10.1109/ACCESS.2016.2617282
  8. Wei Lu, Zheng Tong and Jinghui Chu, "Dynamic Hand Gesture Recognition With Leap Motion Controller," IEEE Signal Processing Letters, vol. 23, no. 23, pp. 1188-1192, Sept. 2016. https://doi.org/10.1109/LSP.2016.2590470
  9. Guillaume Plouffe, Ana-Maria Cretu, "Static and Dynamic Hand Gesture Recognition in Depth Data Using Dynamic Time Warping," IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 2, pp. 305-316, Nov. 2015. https://doi.org/10.1109/TIM.2015.2498560
  10. Elisa Morganti, Leonardo Angelini, Andrea Adami, Denis Lalanne, Leandro Lorenzelli and Elena Mugellinib, "A Smart Watch with Embedded Sensors to Recognize Objects, Grasps and Forearm Gestures," IRIS. International Symposium on Robotics and Intelligent Sensors 2012. Kuching, Sarawak, Malaysia, Sept. 2012.
  11. Georgi, Marcus, Christoph Amma, and Tanja Schultz, "Recognizing Hand and Finger Gestures with IMU based Motion and EMG based Muscle Activity Sensing," Proceedings of the International Conference on Bio-inspired Systems and Signal Processing, Lisbon, Portugal, Jan. 2015.
  12. Yuanhao Wu, Ken Chen and Chenglong Fu, "Natural Gesture Modelingand Recognition Approach Based on Joint Movements and Arm Orientations," IEEE Sensors Journal, vol. 16, no. 21, pp. 7753-7761, Aug. 2016. https://doi.org/10.1109/JSEN.2016.2599019
  13. Hojun Yeom, Hodong Park, Young-Hui Chang, Youngchol Park and Kyoung-Joung Lee, "Stimulus Artifact Suppression Using the Stimulation Synchronous Adaptive Impulse Correlated Filter for Surface EMG Application," Journal of Electrical Engineering & Technology, vol. 7, no 3, pp. 451-458, May 2012. https://doi.org/10.5370/JEET.2012.7.3.451
  14. K. R. Wheeler and C. C. Jorgensen, "Gestures as input: Neuroelectric joysticks and keyboards," IEEE Trans. Pervasive Comput., vol. 2, no. 2, pp. 56-61, Apr./Jun. 2003.
  15. M. Khezri and M. Jahed, "A neuro-fuzzy inference system for sEMGbased identification of hand motion commands," IEEE Trans. Ind. Electron., vol. 58, no. 5, pp. 1952-1959, May 2011. https://doi.org/10.1109/TIE.2010.2053334
  16. Y. Oonishi, S. Oh and Y. Hori, "A new control method for power-assisted wheelchair based on the surface myoelectric signal," IEEE Trans. Ind. Electron., vol. 57, no. 9, pp. 3191-3196, Sep. 2010. https://doi.org/10.1109/TIE.2010.2051931
  17. Pyeong-Gook Jung, Gukchan Lim, Seonghyok Kim and Kyoungchul Kong, "A Wearable Gesture Recognition Device for Detecting Muscular Activities Based on Air-Pressure Sensors," IEEE Transactions on Industrial Informatics, vol. 11, no. 2, pp. 485-494, Feb. 2015. https://doi.org/10.1109/TII.2015.2405413
  18. M. Kreil, G. Ogris, and P. Lukowicz, "Muscle activity evaluation using force sensitive resistors," in Proc. Int. Symp. Med. Devices Biosens, Hong Kong, China, Dec. 2008.
  19. G. Ogris, M. Kreil, and P. lukowicz, "Using FSR based muscule activity monitoring to recognize manipulative arm gestures," in Proc. IEEE Int. Symp. Wearable Comput, Boston, MA, USA, Oct. 2007.
  20. P. Lukowicz, F. Hanser, C. Szubski, and W. Schobersberger, "Detecting and interpreting muscle activity with wearable force sensors," in Proc. 4th Int. Conf. Pervasive Comput., Dublin, Ireland, May 2006.
  21. Seo Yul Kim, Hong Gul Han, Jin Woo Kim, Sanghoon Lee and Tae Wook Kim, "Predictive control estimating operator's intention for steppingup motion by exo-skeleton type power assist system HAL," IEEE Sensors Journal, vol. 17, no 10, pp. 2975-2976, March 2017. https://doi.org/10.1109/JSEN.2017.2679220