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Enabling Always-Available Input with

T. Scott Saponas

, Desney S. Tan

Compuer Science and Engineering

DUB Group

University of Washington

{ssaponas, landay}@cs.washington.edu

ABSTRACT

Previous work has demonstrated the viability of applying

offline analysis to interpret forearm electromyography

(EMG) and classify finger gestures on a physical surface.

We extend those results to bring us closer to using muscle-

computer interfaces for always-available input in real-world

applications. We leverage existing taxonomies of natural

human grips to develop a gesture set covering interaction in

free space even when hands are busy with other objects. We

present a system that classifies these gestures in real-time

and we introduce a bi-manual paradigm that enables use in

interactive systems. We report experimental results demon-

strating four-finger classification accuracies averaging 79%

for pinching, 85% while holding a travel mug, and 88%

when carrying a weighted bag. We further show generali-

zability across different arm postures and explore the tra-

deoffs of providing real-time visual feedback.

H.1.2 [User/Machine Systems]; H.5.2

ACM Classification:

[User Interfaces]: Input devices and strategies; B.4.2 [In-

put/Output Devices]: Channels and controllers

Design, Human Factors

General terms:

Electromyography (EMG), Muscle-Computer

Keywords:

Interface, input, interaction.

INTRODUCTION

Our hands and our ability to control them have evolved

over thousands of years, yielding an amazing ability to pre-

cisely manipulate tools. As such, we have often crafted our

environments and technologies to take advantage of this

ability. For example, many current computer interfaces re-

quire manipulating physical devices such as keyboards,

mice, and joysticks. Even future looking research systems

often focus on physical input devices [5]. However, as

computing environments become more diverse, we often

find ourselves in scenarios where we either cannot, or pre-

fer not to, explicitly interact with a physical device in hand.

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UIST'09, October 4–7, 2009, Victoria, British Columbia, Canada.

Muscle-Computer Interfaces

, Dan Morris

{desney, dan}@microsoft.com

Microsoft Corporation

[email protected]

, Ravin Balakrishnan

Microsoft Research

Previous work has explored hands-free and implement-free

input techniques based on a variety of sensing modalities.

For example, computer vision enables machines to recog-

nize faces, track movement and gestures, and reconstruct

3D scenes [24]. Similarly, speech recognition allows for

hands-free interaction, enabling a variety of speech-based

desktop and mobile applications [8, 11]. However, these

technologies have several inherent limitations. First, they

require observable interactions that can be inconvenient or

socially awkward. Second, they are relatively sensitive to

environmental factors such as light and noise. Third, in the

case of computer vision, sensors that visually sense the en-

vironment are often susceptible to occlusion.

We assert that computer input systems can leverage the full

bandwidth of finger and hand gestures without requiring the

user to manipulate a physical transducer. In this paper, we

show how forearm electromyography (EMG) can be used to

detect and decode human muscular movement in real time,

thus enabling interactive finger gesture interaction. We en-

vision that such sensing can eventually be achieved with an

unobtrusive wireless forearm EMG band (see Figure 1).

Previous work exploring muscle-sensing for input has pri-

marily focused either on using a single large muscle (rather

than the fingers) [2, 3, 4, 22, 25], which does not provide

the breadth of input signals required for computer input,

and/or on situations where the hand and arm are constrained

to a surface [3, 4, 15, 21, 23, 25], which is not a realistic

usage scenario for always-available input devices. Saponas

et al. [18] demonstrated the feasibility of using offline ma-

chine learning techniques to interpret forearm muscle-

sensing and classify finger gestures on a surface. We extend

their offline classification results to achieve online classifi-

Figure 1. Artist rendering of a forearm muscle-sensing band.

, Jim Turner

, James A. Landay

Department of

Computer Science

University of Toronto

[email protected]