Demonstrating the feasibility of using forearm electromyography for muscle-computer interfaces

ABSTRACT

We explore the feasibility of muscle-computer interfaces (muCIs): an interaction methodology that directly senses and decodes human muscular activity rather than relying on physical device actuation or user actions that are externally visible or audible. As a first step towards realizing the mu-CI concept, we conducted an experiment to explore the potential of exploiting muscular sensing and processing technologies for muCIs. We present results demonstrating accurate gesture classification with an off-the-shelf electromyography (EMG) device. Specifically, using 10 sensors worn in a narrow band around the upper forearm, we were able to differentiate position and pressure of finger presses, as well as classify tapping and lifting gestures across all five fingers. We conclude with discussion of the implications of our results for future muCI designs.

REFERENCES

• 1: Barry, D.T., Gordon, K.E. & Hinton, G.G. (1990). Acoustic and surface EMG diagnosis of pediatric muscle disease. Muscle & Nerve, 13(4), 286--290.
• 2: Basmajian, J.V., & De Luca, C.J. (1985). Muscles alive: Their functions revealed by electromyography, 5th edition. Williams & Wilkins: Fort Worth, Texas.
• 3: Biosemi. http://www.biosemi.com.
• 4: Christopher J. C. Burges, A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, v.2 n.2, p.121-167, June 1998 [doi>10.1023/A:1009715923555]
• 5: Enrico Costanza , Samuel A. Inverso , Rebecca Allen, Toward subtle intimate interfaces for mobile devices using an EMG controller, Proceedings of the SIGCHI conference on Human factors in computing systems, April 02-07, 2005, Portland, Oregon, USA [doi>10.1145/1054972.1055039]
• 6: Enrico Costanza , Samuel A. Inverso , Rebecca Allen , Pattie Maes, Intimate interfaces in action: assessing the usability and subtlety of emg-based motionless gestures, Proceedings of the SIGCHI conference on Human factors in computing systems, April 28-May 03, 2007, San Jose, California, USA [doi>10.1145/1240624.1240747]
• 7: Hartmann, B., Benson, M., Junger, A., Quinzio, L., Röhrig, R., Fengler B., Färber, U.W., Wille B., & Hem-pelmann, G. (2004). Computer keyboard and mouse as a reservoir of pathogens in intensive care unit. Journal of Clinical Monitoring and Computing, 18(1), 7--12.
• 8: Hernandez, A., Katoh, R., Yokoi, H., & Yu, W. (2006). Development of a multi-DOF electromyography prosthetic system using the adaptive joint mechanism. Applied Bionics and Biomechanics, 3(2), 101--112.
• 9: S. C. Jacobsen , R. B. Jerard, Computational requirements for control of the utah arm, Proceedings of the 1974 annual conference, p.149-155, January 1974 [doi>10.1145/800182.810395]
• 10: Jiang, M.W., Wang, R.C., Wang, J.Z., & Jin, D.W. (2005). A method of recognizing finger motion using wavelet transform of surface EMG signal. Intl Conf. of the IEEE EMBS, 1(7), 2672--2674.
• 11: Kiguchi, K., Tanaka, T., & Fukuda, T. (2004). Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Trans. on Fuzzy Systems, 12(4), 481--490.
• 12: Kine Biomedical Engineering. http://www.kine.is.
• 13: Lanyi, X., & Adler, A. (2004). An improved method for muscle activation detection during gait. Canadian Conf. on Electrical and Computer Engineering, 357--360.
• 14: Johnny Chung Lee , Desney S. Tan, Using a low-cost electroencephalograph for task classification in HCI research, Proceedings of the 19th annual ACM symposium on User interface software and technology, October 15-18, 2006, Montreux, Switzerland [doi>10.1145/1166253.1166268]
• 15: Manabe, H., Hiraiwa, A. & Sugimura, T. (2003). A ring-shaped EMG measurement system for applying to user interface. Intl. Conf. of the IEEE EMBS, 3020--3023.
• 16: Regan L. Mandryk , M. Stella Atkins , Kori M. Inkpen, A continuous and objective evaluation of emotional experience with interactive play environments, Proceedings of the SIGCHI conference on Human Factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada [doi>10.1145/1124772.1124926]
• 17: Merletti, R., & Parker, P.A. (2004). Electromyography: Physiology, engineering, and noninvasive applications. John Wiley & Sons: Hoboken, New Jersey.
• 18: Moseley, J.B., Jobe, F.W., Pink, M., Perry, J., & Tibone, J. (1992). EMG analysis of the scapular muscles during a shoulder rehabilitation program. The American Journal of Sports Medicine, 20, 128--134.
• 19: Ganesh R. Naik , Dinesh Kant Kumar , Vijay Pal Singh , Marimuthu Palaniswami, Hand gestures for HCI using ICA of EMG, Proceedings of the HCSNet workshop on Use of vision in human-computer interaction, p.67-72, November 01-01, 2006, Canberra, Australia
• 20: Peleg, D., Braiman, E., Yom-Tov, E., & Inbar, G. (2002). Classification of finger activation for use in a robotic prosthesis arm. IEEE Trans. on Neural Systems and Rehabilitation Engineering, 10(4), 290--293.
• 21: Platt, J. (1998). Sequential Minimal Optimization: A fast algorithm for training support vector machines. Microsoft Research Technical Report MSR-TR-98-14.
• 22: Raez, M.B.I., Hussain, M.S., & Mohd-Yasin, F. (2006). Techniques of EMG signal analysis: detection, processing, classification, and applications. Biological Procedures Online, 8, 11--35.
• 23: Schieber, M.H. (1995). Muscular production of individual finger movements: the roles of extrinsic finger muscles. Journal of Neuroscience, 15(1), 248--297.
• 24: Kevin R. Wheeler , Charles C. Jorgensen, Gestures as Input: Neuroelectric Joysticks and Keyboards, IEEE Pervasive Computing, v.2 n.2, p.56-61, April 2003 [doi>10.1109/MPRV.2003.1203754]
• 25: Ian H. Witten , Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems), Morgan Kaufmann Publishers Inc., San Francisco, CA, 2005

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)
          Subjects: Input devices and strategies (e.g., mouse, touchscreen)

Additional Classification:
  B. Hardware
  B.4 INPUT/OUTPUT AND DATA COMMUNICATIONS
      B.4.2 Input/Output Devices
          Subjects: Channels and controllers

General Terms:
Human Factors

Keywords:
electromyography, emg, input, interaction, muscle-computer interfaces