Toward subtle intimate interfaces for mobile devices using an EMG controller

Costanza, Enrico; Inverso, Samuel A.; Allen, Robert B.

doi:10.1145/1054972.1055039

Cited by 74 publications

(52 citation statements)

References 22 publications

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“…Along similar lines, Constanza et al [5,6] have investigated subtle EMG-based interfaces. Users of their system cycle through an auditory voice mail menu on a cell phone by flexing one or both biceps.…”

Section: Emg For Human Computer Interactionmentioning

confidence: 97%

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

Saponas

Tan

Morris

et al. 2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

231

147

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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.

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Section: Emg For Human Computer Interactionmentioning

confidence: 97%

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

Saponas

Tan

Morris

et al. 2008

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

231

147

View full text Add to dashboard Cite

show abstract

“…Dubost and Tanaka employ EMG signals for interaction in musical performance [7]. Other systems include myoelectric sensing as an additional input method [8] or solely as a means to enhance interaction with an existing system [9] [10].…”

Section: Related Workmentioning

confidence: 99%

RemoteHand: A Wireless Myoelectric Interface

Attenberger

Buchenrieder

2014

Human-Computer Interaction. Advanced Interaction Modalities and Techniques

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Abstract. While myoeletric signals (MES) have long been employed for actuating hand prostheses, their potential as novel input for the interaction with computer systems has received little attention up until now. In this contribution, we present RemoteHand, a system that fosters remote device control through the transmission of myoelectric data over WLAN. This allows to manipulate objects through the user's muscle activity regardless of their physical location. In our setup, a mechanical hand is controlled through electromyographic (EMG) sensors placed over the user's forearm muscles. This approach is compared to a conventional remote device control exercised by a tablet touchpad. The results of our user study show that wireless interaction through myoelectric signals is a valid approach. Study participants achieved interaction speeds equal to those of a standard input method. Users especially value myoelectric input with regard to novelty and stimulation.

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“…A Personal Area Network would be used to link these sensors to the device and allow the user to control an interface with the need to touch the device. Costanza et al [6] describe such an interface using electromyogram (EMG) based interactions. A device monitoring activity through EMG is mounted on a user's upper arm.…”

Section: Non-hand Based Gesturingmentioning

confidence: 99%

Head tilting for interaction in mobile contexts

Crossan

McGill

Brewster

et al. 2009

Proceedings of the 11th International Conference on Human-Computer Interaction With Mobile Devices and Services

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Developing interfaces for mobile situations requires that devices are useable on the move. Here, we explore head tilting as an input technique to allow a user to interact with a mobile device 'hands free'. A Fitts' Law style evaluation is described where a user acquires targets, moving the cursor by head tilt. We explored position and velocity control cursor mechanisms in both static and mobile situations to see which provided the best level of performance. Results show that participants could successfully acquire targets using head tilting. Position control was shown to be significantly faster and more accurate in a static context, but exhibited significantly poorer accuracy and longer target acquisition times when the user was on the move. We further demonstrate how analysis of user's gait shows consistent targeting biases at different stages in the gait cycle.

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Toward subtle intimate interfaces for mobile devices using an EMG controller

Cited by 74 publications

References 22 publications

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

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

RemoteHand: A Wireless Myoelectric Interface

Head tilting for interaction in mobile contexts

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