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Analysis and Considerations of the Controllability of EMG-Based Force Input

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Human-Computer Interaction (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14011))

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Abstract

Using electromyography (EMG) measurements for user interfaces (UIs) is widely employed as an interaction method. Some advantages of using EMG-based input are that it does not require a physical controller and can be operated intuitively with small body movements. Existing work has explored different novel interaction methods for UIs using EMG. However, it is still unclear how precisely users can control the force and what kind of control pattern is easier for them to use. Thus, this paper analyzes the effect of EMG-based force input on control accuracy and mental workload. We constructed a pointer-tracking application that inputs force strength from forearm EMG. Tracking accuracy and mental workload were evaluated under the conditions of multiple tracking patterns and hand gestures. The results showed that EMG-based input accuracy was affected by the way in which the force was applied (e.g., strengthened, weakened, or fluctuated). We also found that hand gesture type did not influence accuracy or mental workload.

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References

  1. Beniczky, S., Conradsen, I., Henning, O., Fabricius, M., Wolf, P.: Automated real-time detection of tonic-clonic seizures using a wearable EMG device. J. Neurol. 90, 428–434 (2018)

    Google Scholar 

  2. Benalcázar, M.E., Jaramillo, A.G., Jonathan, A.Z., Páez, A., Andaluz, V.H.: Hand gesture recognition using machine learning and the Myo armband. In: Proceedings of European Signal Processing Conference (EUSIPCO), pp. 1040–1044 (2017)

    Google Scholar 

  3. Saponas, T.S., Tan, D.S., Morris, D., Balakrishnan, R., Turner, J., Landay, J.A.: Enabling always-available input with muscle-computer interfaces. In: Proceedings of ACM Symposium on User Interface Software and Technology (UIST), pp. 167–176 (2009)

    Google Scholar 

  4. Xia, P., Hu, J., Peng, Y.: EMG-based estimation of limb movement using deep learning with recurrent convolutional neural networks: EMG-based estimation of limb movement. Artif. Organs 42(5), E67–E77 (2018). https://doi.org/10.1111/aor.13004

    Article  Google Scholar 

  5. Zhang, Q., Hosoda, R., Venture, G.: Human joint motion estimation for electromyography (EMG)-based dynamic motion control. In: Proceedings of IEEE Engineering in Medicine and Biology Society (EMBC), pp. 21–24 (2013)

    Google Scholar 

  6. Becker, V., Oldrati, P., Barrios, L., Sörös, G.: Touchsense: classifying finger touches and measuring their force with an electromyography armband. In: Proceedings of ACM International Symposium on Wearable Computers (ISWC), pp. 1–8 (2018)

    Google Scholar 

  7. Hartmann, B., et al.: Computer keyboard and mouse as a reservoir of pathogens in an intensive care unit. J. Clin. Monitor. Comput. 18, 7–12 (2004)

    Article  Google Scholar 

  8. Greenberg, S., Fitchett, C.: Phidgets: easy development of physical interfaces through physical widgets. In: Proceedings of ACM symposium on User Interface Software and Technology (UIST), pp. 167–176 (2001)

    Google Scholar 

  9. Chu, J.U., Moon, I., Lee, Y.J., Kim, S.K., Mun, M.S.: A supervised feature-projection-based real-time EMG pattern recognition for multifunction myoelectric hand control. IEEE/ASME Trans. Mechatron. 12(3), 282–290 (2007)

    Article  Google Scholar 

  10. Ngeo, J.G., Tamei, T., Shibata, T.: Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscle activation model. J. Neuroeng. Rehabil. 11(1), 1–14 (2014)

    Article  Google Scholar 

  11. Benko, H., Saponas, T.S., Morris, D., Tan, D.: Enhancing input on and above the interactive surface with muscle sensing. In: Proceedings of ACM Interactive Tabletops and Surfaces (ITS), pp. 93–100 (2009)

    Google Scholar 

  12. Rosen, J., Brand, M., Fuchs, M., Arcan, M.: A myosignal-based powered exoskeleton system. IEEE Trans. Syst. Man Cybern. Part A: Syst. Humans. 31(3), 210–222 (2001)

    Google Scholar 

  13. Rakasena, E.P.G., Herdiman, L.: Electric wheelchair with forward-reverse control using Electromyography (EMG) control of arm muscle. J. Phys. Conf. Ser. 1450(1), 1–7 (2020)

    Article  Google Scholar 

  14. Raurale, S.A., McAllister, J., del Rincon, J.M.: Real-time embedded EMG signal analysis for wrist-hand pose identification. IEEE Trans Signal Process. 68, 2713–2723 (2020)

    Article  MATH  Google Scholar 

  15. Fridlund, J., Schwartz, G.E., Fowler, S.C.: Pattern recognition of self-reported emotional state from multiple-site facial EMG activity during affective imagery. J. Psychophysiol. 21, 622–637 (1984)

    Article  Google Scholar 

  16. Winter, D.A., Yack, H.J.: EMG profiles during normal human walking: stride-to-stride and inter-subject variability. J. Electroencephalogr. Clin. Neurophysiol. 402–411 (1987)

    Google Scholar 

  17. Yamagami, M., Steele, K.M., Burden, S.A.: Decoding intent with control theory: comparing muscle versus manual interface performance. In: Proceedings of CHI Conference on Human Factors in Computing Systems (CHI), pp. 1–12 (2020)

    Google Scholar 

  18. Lobo-Prat, J., Keemink, A.Q., Stienen, A.H., Schouten, A.C., Veltink, P.H., Koopman, B.F.: Evaluation of EMG, force and joystick as control interfaces for active arm supports. J. Neuroeng. Rehabil. 11, 1–13 (2014)

    Article  Google Scholar 

  19. Corbett, E.A., Perreault, E.J., Kuiken, T.A.: Comparison of electromyography and force as interfaces for prosthetic control. J. Rehabil. Res. Dev. 48, 629–641 (2011)

    Article  Google Scholar 

  20. Hart, S.G., Staveland, L.E.: Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. J. Adv. Psychol. 52, 139–183 (1988)

    Article  Google Scholar 

  21. Ishikawa, K., Akita, J., Toda, M., Kondo, K., Sakurazawa, S., Nakamura, Y.: Robust finger motion classification using frequency characteristics of surface electromyogram signals. In: Proceedings of International Conference on Biomedical Engineering (ICoBE), pp. 362–367 (2012)

    Google Scholar 

  22. Jensen, C., Vasseljen, O., Westgaard, R.H.: The influence of electrode position on bipolar surface electromyogram recordings of the upper trapezius muscle. J. Appl. Physiol. Occup. Physiol. 67, 266–273 (1993)

    Article  Google Scholar 

  23. Ozdemir, M.A., Kisa, D.H., Guren, O., Onan, A., Akan, A.: EMG based hand gesture recognition using deep learning. In: Proceedings of Medical Technologies Congress, pp. 1–4 (2020)

    Google Scholar 

  24. Saponas, T.S., Tan, D.S., Morris, D., Turner, J., Landay, J.A.: Making muscle-computer interfaces more practical. In: Proceedings of SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 851–854 (2010)

    Google Scholar 

  25. Rautaray, S.S., Agrawal, A.: A novel human computer interface based on hand gesture recognition using computer vision techniques. In: Proceedings of Intelligent Interactive Technologies and Multimedia (IITM), pp. 292–296 (2010)

    Google Scholar 

  26. Byers, J.C., Bittner, A., Hill, S.: Traditional and raw Task Load Index (TLX) correlations: are paired comparisons necessary? Advances in Industrial Ergonomics and Safety l, pp. 481–485. Taylor and Francis (1989)

    Google Scholar 

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Authors and Affiliations

  1. Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan

    Hayato Nozaki & Yuta Kataoka

  2. Global Innovation Research Organization, Ritsumeikan University, Ibaraki, Osaka, Japan

    Christian Arzate Cruz

  3. College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, Japan

    Fumihisa Shibata & Asako Kimura

Authors
  1. Hayato Nozaki
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  2. Yuta Kataoka
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  3. Christian Arzate Cruz
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  4. Fumihisa Shibata
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  5. Asako Kimura
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Correspondence to Hayato Nozaki .

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Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Hosei University, Tokyo, Japan

    Ayako Hashizume

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Nozaki, H., Kataoka, Y., Arzate Cruz, C., Shibata, F., Kimura, A. (2023). Analysis and Considerations of the Controllability of EMG-Based Force Input. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14011. Springer, Cham. https://doi.org/10.1007/978-3-031-35596-7_36

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  • DOI: https://doi.org/10.1007/978-3-031-35596-7_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35595-0

  • Online ISBN: 978-3-031-35596-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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