skip to main content
10.1145/2702123.2702461acmconferencesArticle/Chapter ViewAbstractPublication PageschiConference Proceedingsconference-collections
research-article

Proprioceptive Interaction

Published:18 April 2015Publication History

ABSTRACT

We propose a new way of eyes-free interaction for wearables. It is based on the user's proprioceptive sense, i.e., rather than seeing, hearing, or feeling an outside stimulus, users feel the pose of their own body. We have implemented a wearable device called Pose-IO that offers input and output based on proprioception. Users communicate with Pose-IO through the pose of their wrists. Users enter information by performing an input gesture by flexing their wrist, which the device senses using a 3-axis accelerometer. Users receive output from Pose-IO by find-ing their wrist posed in an output gesture, which Pose-IO actuates using electrical muscle stimulation. This mechanism allows users to interact with Pose-IO without visual or auditory senses, but through the proprioceptive sense alone. We developed three simple applications that demonstrate symmetric proprioceptive interaction, where input and output occur through the same limb, as well as asymmetric interaction, where input and output occur through different limbs. In a first user study, participants using a symmetric proprioceptive interface re-entered poses received from Pose-IO with an average accuracy of 5.8° despite the minimal bandwidth offered by the device. In a second, exploratory study, we investigated participants' emotional response to asymmetric proprioceptive interaction and the concept of the user's body serving as interface. Participants reported to enjoy the experience (4.6 out of 5).

Skip Supplemental Material Section

Supplemental Material

pn1766-file3.mp4

mp4

80.3 MB

References

  1. Ashbrook, D. Enabling Mobile Microinteractions. Ph.D. Dissertation. Georgia Tech, Atlanta, GA, USA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Ban, Y., Narumi, T., Fujii, T., Sakurai, S., Imura, J., Tanikawa, T., and Hirose, M Augmented endurance: controlling fatigue while handling objects by affecting weight perception using augmented reality. Proc. CHI '13, 69--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Baudisch, P., Pohl, H., Reinicke, S., Wittmers, E., Lühne, P., Knaust, M., Köhler, S., Schmidt, P., and Holz, C. Imaginary reality gaming: ball games without a ball. Proc. UIST'13, 405--410. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Boven, K., Fejtl, M., Moeller, A., Nisch, W. and Stett, A. On Micro-Electrode Array Revival. In: Advances in Network Electrophysiology Using Multi-Electrode Arrays, 2006: 24--37.Google ScholarGoogle Scholar
  5. Chao, E., An, K., Cooney, W., Linscheid, R. Biomechanics of the Hand. World Scientific Publishing, 1989.Google ScholarGoogle ScholarCross RefCross Ref
  6. Daniel, S., Gallagher, S., (Eds.), Handbook of Phenomenology and Cognitive Science, Springer; 2010 edition, ISBN-10: 9048126452Google ScholarGoogle Scholar
  7. E. Gardner and J. Martin. Coding of Sensory Information, Principles of Neural Science. McGrawHill, fourth edition, 2000.Google ScholarGoogle Scholar
  8. Farbiz, F., Yu, Z. H., Manders, C., and Ahmad, W. An electrical muscle stimulation haptic feedback for mixed reality tennis game. Proc. SIGGRAPH '07 (poster). Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Felzer, T., and Nordmann, R. Using intentional muscle contractions as input signals for various hands-free control applications. Proc. iCREATe'08, 87--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Gandevia, S., Smith, J., Crawford, M., Proske, U., and Taylor, J. Motor commands contribute to human position sense. Journal of Physiology, 2006, 571(3) 703--710.Google ScholarGoogle ScholarCross RefCross Ref
  11. Gustafson, S., Bierwirth, D., and Baudisch, P. Imaginary interfaces: spatial interaction with empty hands and without visual feedback. Proc. UIST '10, 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Gustafson, S., Rabe, B., and Baudisch, P. Understanding palm-based imaginary interfaces: the role of visual and tactile cues when browsing. Proc CHI'13, 889--898. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Harrison, C., Tan, D., and Morris, D.. Skinput: appropriating the body as an input surface. Proc. CHI'10, 453--462. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Hollerbach J. and Jacobsen S. Haptic Interfaces for Teleoperation and Virtual Environments. Proc. of First Workshop on Simulation and Interaction in Virtual Environments '95, 13--15.Google ScholarGoogle Scholar
  15. Hook, J., Nappey, T., Making 3D Printed Objects Interactive Using Wireless Accelerometers. Proc. CHI '14 Extended Abstracts, 1435--1440. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. Bark, J. W. Wheeler, S. Premakumar, and M. R. Cutkosky, Comparison of Skin Stretch and Vibrotactile Stimulation for Feedback of Proprioceptive Information. Proc. HAPTICS '08, 71--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Karuei, I., MacLean, K., Foley-Fisher, Z., MacKenzie, R., Koch, S., and El-Zohairy, M. Detecting vibrations across the body in mobile contexts. Proc. CHI'11, 3267--3276. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Katoh, M, Nishimura, N., Yokoyama, M., Hachisu, T., Sato, M., FUnited Kingdomushima, S., and Kajimoto, H. Optimal selection of electrodes for muscle electrical stimulation using twitching motion measurement. Proc. AH '13, 237--238. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Ken Hinckley, Randy Pausch, and Dennis Proffitt. 1997. Attention and visual feedback: the bimanual frame of reference. Proc. I3D '97, 121--126. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Kruijff, E., Schmalstieg, D., and Beckhaus, S. Using neuromuscular electrical stimulation for pseudo-haptic feedback. Proc. VRST '06, 316--319. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Li, K, A. Designing easily learnable eyes-free interaction, Ph.D. Thesis. UC San Diego, 2009.Google ScholarGoogle Scholar
  22. Lopes, P. and Baudisch, P., Muscle-Propelled Force Feedback: bringing force feedback to mobile devices, Proc. CHI'13, 2577--2580. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Maffiuletti, N., Minetto, M., Farina, D., and Bottinelli, R. Electrical stimulation for neuromuscular testing and training: State-of-the art and unresolved issues. In Journal of Applied Physiology 2011, 111:2391--2397.Google ScholarGoogle Scholar
  24. Murayama, J., Bougrila, L., Luo, Y., Akahane, K., Hasegawa, S., Hirsbrunner, B., Sato, M. SPIDAR G&G: a two-handed haptic interface for bimanual VR interaction. Proc. EuroHaptics '04, 138--146.Google ScholarGoogle Scholar
  25. Ni, T. and Baudisch, P. Disappearing mobile devices. Proc. UIST'09, 101--110. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Pasquero, J., Stobbe, S., and Stonehouse, N. A haptic wristwatch for eyes-free interactions. Proc. CHI'11, 3257--3266. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pfeiffer, M., Schneegass, S., Alt, F., and Rohs, M., Let Me Grab This: A Comparison of EMS and Vibration for Haptic Feedback in Free-Hand Interaction, Proc. AH'14. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Proske, U., and Gandevia, S., The Proprioceptive Senses: Their Roles in Signaling Body Shape, Body Position and Movement, and Muscle Force, In Physiological Reviews 2012, Vol. 92 no. 4,1651--1697Google ScholarGoogle Scholar
  29. Red Hands, http://en.wikipedia.org/wiki/Red_hands.Google ScholarGoogle Scholar
  30. Roudaut, A., Rau, A., Sterz, C., Plauth, M., Lopes, P., and Baudisch, P. Gesture output: eyes-free output using a force feedback touch surface. Proc. CHI'13, 25472556. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Saponas, S., Tan, D., Morris, D., Balakrishnan, R., Turner, J., and Landay, J. Enabling always-available input with muscle-computer interfaces. Proc. UIST'09, 167--176. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Stelarc. From zombies to cyborg bodies: exoskeleton, extra ear and avatars. Proc. C&C '99, 23. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Strojnik, P., Kralj, A., and Ursic, I., Programmed sixchannel electrical stimulator for complex stimulation of leg muscles during walking, IEEE Trans. Biomed. Eng. 26, 112, 1979.Google ScholarGoogle ScholarCross RefCross Ref
  34. Tamaki, E., Miyaki, T., and Rekimoto, J., Possessed Hand: techniques for controlling human hands using electrical muscles stimuli. Proc. CHI '11, 543--552. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Terrault, S., Lecolinet, E., Eagan, J., and Guiard, Y. Watchit: simple gestures and eyes-free interaction for wristwatches and bracelets. Proc. CHI'13, 1451--1460. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. TsetserUnited Kingdomou, D., Sato, K., and Tachi, S. ExoInterfaces: novel exosceleton haptic interfaces for virtual reality, augmented sport and rehabilitation. Proc. AH '10, 1--6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Walsh, L., Smith, J., Gandevia, S, and Taylor, J. The combined effect of muscle contraction history and motor commands on human position sense. Journal of Experimental Brain Research, 2009; 195(4): 603--10.Google ScholarGoogle Scholar
  38. Wigdor, D., and Wixon, D, Brave NUI World: Designing Natural User Interfaces for Touch and Gesture. Morgan Kaufmann Publishers Inc., 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Proprioceptive Interaction

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems
      April 2015
      4290 pages
      ISBN:9781450331456
      DOI:10.1145/2702123

      Copyright © 2015 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 18 April 2015

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      CHI '15 Paper Acceptance Rate486of2,120submissions,23%Overall Acceptance Rate6,199of26,314submissions,24%

      Upcoming Conference

      CHI '24
      CHI Conference on Human Factors in Computing Systems
      May 11 - 16, 2024
      Honolulu , HI , USA

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader