Cub Cadet i1042 Operator's Manual page 2

cation that enables using muscle-sensing for always-
available input in real-world applications that are not con-
strained to a surface. Note that our contribution is not in the
realm of trying to better understand or measure the physiol-
ogy of human musculature, but rather in simply sensing
muscle activity to enable interaction. Specifically:
1. We leverage existing taxonomies of natural human
grips to develop a gesture set covering interaction in
free space, including when the hands are busy with ob-
jects, and even when hands and muscles are under load.
2. We develop a procedure for rapidly and robustly cali-
brating an activation signal, present a system that clas-
sifies our gestures in real-time, and introduce a bi-
manual "select and activate" paradigm that enables use
in interactive systems.
3. We demonstrate the feasibility of our approach through
a laboratory experiment. Results show average classifi-
cation accuracies of 79% for pinching, 85% while
holding a travel mug, and 88% when carrying a
weighted bag, all for four-finger gesture sets. Results
further suggest generalizability across different arm
postures. Furthermore, we show preliminary evidence
of use within a more ecologically valid example appli-
cation: controlling a simulated portable music player.
We conclude the paper with discussion of our results, the
limitations of our techniques, implications for design, and
proposals for future work.
BACKGROUND AND RELATED WORK
Sensing Muscles with EMG
Humans employ a complex set of skeletal muscles and ad-
joining tendons and bones to create body movement. The
brain initiates movement process by transmitting an elec-
trical signal through the nervous system. This signal stimu-
lates the fibers that make up our muscles, which contract in
response to create forces or body movement.
EMG senses this muscular activity by measuring the elec-
trical potential between pairs of electrodes. This can either
be done invasively (with needles in the muscle) or from the
surface of the skin. While invasive EMG can be very accu-
rate, our work focuses on surface EMG because it is more
practical for HCI applications. For more detailed informa-
tion on electromyography, see Merletti et al. [13].
For either modality (surface or invasive), the EMG signal is
an oscillating electrical wave. When a muscle is contracted,
the amplitude of this wave increases, with most of the pow-
er in the frequency range of 5 to 250 Hz [13].
Applications of EMG Sensing
EMG is frequently used in clinical settings for muscle func-
tion assessment during rehabilitation and for measuring
muscle activation to assess gait [9]. In clinical applications,
a typical statistic computed over the EMG signal is the root
mean squared (RMS) amplitude of the measured potential.
This provides a rough metric for how active a muscle is at a
given point in time. For a review of processing techniques
used in previous work, see [14].
EMG is also used in both research and clinical settings for
controlling prosthetics. This typically involves sensing the
activity in large individual muscles and using it as input to
control the movement of physical devices. For example, the
shoulder muscle might be used to control one of the degrees
of freedom in a lower-arm prosthetic. Other work has ex-
plored similar techniques for sensing activity in large mus-
cles such as the biceps or pectoralis for computer input by
healthy individuals (e.g. [2]). However, learning to perform
fine tasks with muscles that are not normally used for dex-
terous manipulation can be difficult.
Recent work has used surface EMG to sense and decipher
muscle activity that drives fine motor function in our fin-
gers, wrists, and hands. Wheeler et al. [23] explore EMG-
based input systems, but assume that the hands are in a con-
strained, static posture, and do not address calibration issues
associated with real-world use. Ju et al. [6] explored several
machine learning approaches to classifying a finger-pinch
gesture using electrodes placed near participants' wrists,
and achieved classification accuracies as high as 78% when
differentiating among four gestures. Their work, however,
was focused on machine learning techniques, and does not
address the human-computer interaction issues that impact
the feasibility of real-world EMG applications. In particu-
lar, their work does not address posture-independence (e.g.,
arm rotation), hands-busy scenarios, scenarios in which
hands are not constrained to a surface, the "Midas Touch"
problem (differentiating intended gestures from rest), or
real-time classification. Our work builds on the work of Ju
et al. by addressing each of these issues.
Saponas et al. [18] used 10 EMG sensors worn in a narrow
band around the upper forearm to differentiate position,
pressure, tapping, and lifting gestures across five fingers
placed on a surface. They showed the effectiveness of using
not only RMS amplitude but also frequency energy and
phase coherence features in a linear classifier to attain com-
pelling proof-of-concept results. However, their work was
limited in that participants were constrained to fixed arm
postures while sitting in a chair and working on a physical
surface. Furthermore their data was processed using offline
analysis, which did not allow exploration of real-time inte-
ractions or the potential effects of feedback to the user.
We seek to extend previous muscle-sensing work to explore
real-time classification of finger-level movement for more
naturalistic settings including when people are holding ob-
jects. We also investigate practical concerns including arm
posture independence, "Midas touch," and visual feedback.
Natural Human Grips
Most of the input devices we use for computing today take
advantage of our ability to precisely operate physical trans-
ducers like buttons, knobs, and sliders. While this is an ex-
cellent approach when a computing device is one's primary
focus, as in desktop computing, physical devices can be