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Estimating Upper Arm sEMG from Wrist PPG

Published:21 September 2021Publication History

ABSTRACT

The surface electromyogram (sEMG) involves the acquisition of muscle-action potentials transmitted by volume conduction from the skin. Surface electrodes require disposable conductive gel or adhesive tape to be attached to the skin, which is costly, and the tape may damage the skin when it is removed. This paper proposes a method for recognizing the muscle-activity state of the arm and a method for estimating sEMG using pulse-wave data (photoplethysmography). From an evaluation experiment with five participants, three types of muscle activity were recognized with 75+% accuracy and sEMG was estimated with approximately 20% error rate.

References

  1. Christoph Amma, Thomas Krings, Jonas Böer, and Tanja Schultz. 2015. Advancing Muscle-Computer Interfaces with High-Density Electromyography. In Proceedings of the 33rd Annual ACM SIGCHI International Conference on Human Factors in Computing Systems (CHI2015). 929–938.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Donny Huang, Xiaoyi Zhang, T. Scott Saponas, James Fogarty, and Shyamnath Gollakota. 2015. Leveraging Dual-Observable Input for Fine-Grained Thumb Interaction Using Forearm EMG. In Proceedings of the 28th Annual ACM Symposium on User Interface Software and Technology (UIST2015). 523–528.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Antonio Fratini, Antonio La Gatta, Paolo Bifulco, Maria Romano, and Mario Cesarelli. 2009. Muscle motion and EMG activity in vibration treatment. Medical engineering and physics 31, 9 (2009), 1166–1172.Google ScholarGoogle Scholar
  4. Gordon L. Warren, Karl M. Hermann, Christopher P. Ingalls, Maria R. Masselli, RB Armstrong. 2000. Decreased EMG median frequency during a second bout of eccentric contractions. Medicine and science in sports and exercise 32, 4 (2000), 820–829.Google ScholarGoogle Scholar
  5. Jess McIntosh, Charlie McNeill, Mike Fraser, Frederic Kerber, Markus Löchtefeld, and Antonio Krüger. 2015. EMPress: Practical Hand Gesture Classification with Wrist-Mounted EMG and Pressure Sensing. In Proceedings of the ACM SIGCHI International Conference on Human Factors in Computing Systems (CHI2015). 2332–2342.Google ScholarGoogle Scholar
  6. Jordyn Ting, Alessandro Del Vecchio, David Friedenberg, Monica Liu, Caroline Schoenewald, Devapratim Sarma, Jennifer Collinger, Sam Colachis, Gaurav Sharma, Dario Farina, Douglas J. Weber. 2019. A wearable neural interface for detecting and decoding attempted hand movements in a person with tetraplegia. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2019). 1930–1933.Google ScholarGoogle ScholarCross RefCross Ref
  7. Tommaso Lenzi, Stefano Marco Maria De Rossi, Nicola Vitiello, and Maria Chiara Carrozza. 2012. Intention-based EMG control for powered exoskeletons. IEEE transactions on biomedical engineering 59, 8 (2012), 2180–2190.Google ScholarGoogle Scholar
  8. Masashi Toda, Junichi Akita, Shigeru Sakurazawa, Keisuke Yanagihara, Mihoko Kunita, and Kunio Iwata. 2006. Wearable biomedical monitoring system using textilenet. In Proceedings of the International Symposium on Wearable Computers (ISWC2006). 119–120.Google ScholarGoogle ScholarCross RefCross Ref
  9. Naoji Matsuhisa, Martin Kaltenbrunner, Tomoyuki Yokota, Hiroaki Jinno, Kazunori Kuribara, Tsuyoshi Sekitani, and Takao Someya. 2015. Printable elastic conductors with a high conductivity for electronic textile applications. Nature communications 6, 1 (2015), 1–11.Google ScholarGoogle Scholar
  10. Jerrold Scott Petrofsky. 1979. Frequency and amplitude analysis of the EMG during exercise on the bicycle ergometer. European Journal of Applied Physiology and Occupational Physiology 41, 1(1979), 1–15.Google ScholarGoogle ScholarCross RefCross Ref
  11. Sourav Chandra, Jinghua Li, Babak Afsharipour, Andres F. Cardona, Nina L. Suresh, Limei Tian, Yujin Deng, Yishan Zhong, Zhaoqian Xie, Haixu Shen, Yonggang Huang, John A. Rogers, William Z. Rymer. 2021. Performance Evaluation of a Wearable Tattoo Electrode Suitable for High-Resolution Surface Electromyogram Recording. IEEE Transactions on Biomedical Engineering 68, 4 (2021), 1389–1398.Google ScholarGoogle ScholarCross RefCross Ref
  12. Yamakoshi K., Shimazu H., Shibata M., Kamiya A.1982. New oscillometric method for indirect measurement of systolic and mean arterial pressure in the human finger (Part 1) Model experiment. Med Biol Eng Comput 20, 3 (1982), 307–313.Google ScholarGoogle ScholarCross RefCross Ref
  13. Yamakoshi K., Shimazu H., Shibata M., Kamiya A.1982. New oscillometric method for indirect measurement of systolic and mean arterial pressure in the human finger (Part 2) Correlation study. Med Biol Eng Comput 20, 3 (1982), 314–318.Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

    cover image ACM Conferences
    ISWC '21: Proceedings of the 2021 ACM International Symposium on Wearable Computers
    September 2021
    220 pages
    ISBN:9781450384629
    DOI:10.1145/3460421

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    • Published: 21 September 2021

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