Cub Cadet i1042 Operator's Manual page 4

form finger gestures while it collected training data. This
data was immediately used to train our gesture recognizer
and build a predictive model. The training phase was fol-
lowed by a testing phase in which the system attempted to
classify the participant's gestures in real-time. Part C used
the training data collected in Part B for real-time control.
In a real-world interactive system, determining when a user
is performing a gesture and when he is not is crucial for
preventing spurious detection of gestures and precisely
labeling gesture onset or offset. This is particularly true if
there is a strong timing component to the application, such
as in games. Even in applications that do not have an intrin-
sic timing component, such as text entry, ambiguities in
timing can yield incorrect results. For example, when
switching from pinching with the index finger to the ring
finger, a user passes through intermediate states, which may
cause spurious or incorrect classifications of user intention.
Our approach to differentiating gesture from rest, and to
simultaneously increasing the precision of gesture timing, is
to introduce an explicit activation gesture. To do this, we
use a second muscle-interface source, making a fist and
squeezing the contra-lateral hand, in this case the non-
dominant hand. Squeezing is a large multi-muscle action
that can be robustly detected with consistent timing, but in
itself is not sufficiently complex for most applications. By
combining rich gestures performed with one hand and ro-
bust but simple gestures performed with the other hand, we
allow reliable and precise muscle-based interactions.
In addition to making the timing of input more predictable,
using the non-dominant hand for gesture activation also
allows the user to rapidly re-execute a single gesture many
times in a row. For example, when scrolling through a list,
the "down" gesture can be held for a second while the non-
dominant hand makes several quick squeezes. This bima-
nual "select and activate" paradigm is the one we used in
the testing phase of our experiment.
Figure 3. Our finger gesture sets. a) pinch gestures performed
in three different arm postures b) fingers squeezing a travel
mug c) fingers pulling up against the handle of a carried bag
Part A: Hands-Free Finger Gestures
The first part of our experiment explored performing finger
gestures when the hands were not holding anything. Each
participant performed pinch gestures with the thumb and
one of the other fingers of their dominant hand. The gestur-
ing arm was held in a comfortable position with a bent el-
bow and the empty hand held at about shoulder height (see
Figure 1 and Figure 3a).
Without the constraint of a surface to rest on, people natu-
rally move and rotate their arms and wrists between ges-
tures. Doing so moves muscles under the skin and relative
to the attached sensors, creating changes to the observed
EMG signals and potentially impacting classification. Most
previous work has carefully constrained arm posture to
avoid this scenario (for example, securing people's arm to a
surface). However, this is an unreasonable constraint if
muscle-computer interfaces are to be used for real-world
interaction. Hence, we set out to examine whether or not
our decoding techniques generalize to variable postures,
and more importantly, how we can improve our techniques
to better support posture variability.
We chose three different postures to explore: the two ex-
tremes of comfortable rotation of the forearm toward and
away from their shoulder (pronation and supination) as well
as a "natural" midpoint position (see Figure 3a).
Hands-Free Training Phase
Participants sat in a chair facing a desktop display. The sys-
tem prompted participants to pinch each of their fingers to
their thumb by highlighting the appropriate finger on an
outline of a hand (see Figure 4a). We asked participants to
press "a comfortable amount". If they asked for clarifica-
tion, we told them to "press hard enough to dent a tomato,
but not hard enough to rupture the skin." They were told to
relax their fingers when nothing was highlighted. Fingers
were highlighted for a second, with a break of three-
quarters of a second in between each stimulus.
We employed a block design, with each block comprising
one trial each of an index, middle, ring, and pinky finger
gesture, presented in random order. We gathered 25 blocks
of training data for each of the three arm postures, the order
of which was counterbalanced across participants.
Hands-Free Testing Phase
In the testing phase, participants performed 25 blocks of
gestures in each of the three arm postures. As in the training
Figure 4. (a) A red highlight indicates that a gesture should be
performed with the given finger; (b) a blue highlight indicates
the currently recognized gesture; (c) a purple highlight indi-
cates that the correct gesture is being performed.