Cub Cadet i1042 Operator's Manual page 3

difficult or impossible to use when a user's hands or body
are devoted to another activity. For example, a jogger may
strap a music player to her arm or waist. However, even
simple tasks such as changing songs, channels, or volume
can be a struggle, requiring a user to reach across her body,
possibly stop running, find the right button, and manipulate
it. In circumstances such as these, where a user prefers to
keep their hands free or is already holding something other
than an input device, we propose that muscle-sensing offers
an opportunity to take advantage of our manual dexterity
without requiring physical actuation of a device.
To guide the design of muscle-sensing-based interaction
techniques, it is important to consider the space of natural
human grips and hand postures that we might leverage for
gesture design. Over the last century, many grip posture
classifications have been developed for biomechanical
modeling, robotics, and therapy [12]. Schlesinger [20] put
forth most well-known of these taxonomies (see Figure 2),
characterizing six different manual grasps:
•
Spherical: for holding spherical tools such as balls
•
Cylindrical: for holding cylindrical tools such as cups
•
Palmar: for grasping with palm facing the object
•
Tip: for holding small tools like a pen
•
Lateral: for holding thin, flat objects like paper
•
Hook: for supporting a heavy load such as a bag
We explore techniques that will enable people to interact
with computers when their hands are already being used in
one of these grips, or when their hands are unencumbered
but a handheld device is impractical. We divide these grips
into three classes: small or no object in hand (tip and later-
al), tool in hand (cylindrical, spherical, and palmar), and
heavy load in hand (hook). Based on these three classes we
suggest finger gestures, detect and classify these gestures in
real-time using forearm muscle sensing, develop a two-
handed interaction technique that allows for these gestures
to control applications, and experimentally demonstrate the
efficacy of these gestures.
Figure 2. Schlesinger's natural grip taxonomies [20] as de-
picted in MacKenzie and Iberall [12]. Groupings indicate the
three similarity classes that guide our gesture set.
EXPERIMENT
We conducted a laboratory experiment to investigate using
forearm EMG to distinguish finger gestures within the three
classes of grips: (1) small or no object in hand, (2) tool in
hand, and (3) heavy load in hand.
Participants
Twelve individuals (5 female) volunteered to participate in
the experiment. Participants ranged from 18 to 55 years of
age with an average age of 36. All were daily computer
users, and came from a variety of occupations. None re-
ported existing muscular conditions or skin allergies, and
all were right-handed. None were colorblind and all had
20/20 or corrected-to-20/20 vision. The experiment took 1.5
hours and participants were given a small gratuity.
Equipment and Setup
We used a BioSemi Active Two system as our forearm EMG
sensing device (www.biosemi.com). This system samples
eight sensor channels at 2048 Hz. We first had participants
clean their forearms with a soft scrub solution while we
prepared the BioSemi sensors with conductive gel and ad-
hesive. The preparation, gel and adhesive are artifacts of
our EMG setup and could be eliminated if dry electrodes
such as the Dri-Stik (NeuroDyne Medical, Corp.) are used.
This would clearly be more appropriate for real-world use.
To get the best possible signal, EMG sensing is traditionally
conducted with two sensors spread an inch apart on a mus-
cle belly. However, Saponas et al. [18] showed that they
were able to obtain reasonable results even when not pre-
cisely placing sensors. As such, we chose to place six sen-
sors and two ground electrodes in a roughly uniform ring
around each participant's upper right forearm for sensing
finger gestures. We also placed two sensors on the upper
left forearm for recognizing left-hand squeezes, or activa-
tion intent. This configuration mimics potential use with an
approximately-placed armband EMG device, as illustrated
in Figure 1. Setup took about 15 minutes.
Design and Procedure
We divided the experiment into three parts. Part A ex-
amined gestures when the participant's hand was free of
objects and explored the sensitivity of our techniques to
arm posture. Part B examined gestures while the hands
were busy holding objects, a travel mug and a weighted bag
that created constant muscular load. In Part C, participants
used the muscle-computer interface (while holding an ob-
ject) to control a simulated portable music player.
Before beginning any of the tasks in each session, we per-
formed a short calibration step. Participants squeezed a ball
for four seconds and then relaxed for another four. This
calibration provided us with approximate maximum and
minimum values across each channel and feature, which we
used for normalizing the signal from each channel. Our
normalization process was to scale the signal from zero to
one based on the observed maximum and minimum value.
Parts A and B of the experiment each contained a training
phase, in which the system prompted the participant to per-