Biomedical Engineering Reference
In-Depth Information
was moved forward by some amount. The faster the foot strikes occurred, the faster
the user moved through the virtual scene.
A very early walking-in-place system, called a virtual treadmill, applied a neural net-
work to head tracker data to detect local maxima in stepping-related vertical head-bob
[
35
]. A set amount of forward movement, inserted over several frames, was added
between detected steps. The neural network required four positive “step” signals be-
fore initiating movement and two “no step” signals before stopping. Starting latency
was about two seconds; stopping, about one second.
Other methods of foot-strike detection include pressure sensors in shoes [
36
], a floor-
based array of pressure sensors [
2
], and head-worn accelerometers [
44
]. Unlike the
first two methods which produce a binary variable when a step is detected, the latter
technique generates a stream of accelerometer data in which foot-strikes are detected
as local maxima.
Starting latency is a problem for foot-strike techniques: a step is not recognized until
the foot has been lifted and returned to the ground. For a casual walking speed of
3mph and a 24” step length, this latency is around half a second.
Movement can be implemented by choosing a moderate base speed and computing
the distance the viewpoint must be moved for each foot strike to achieve that speed
through the scene. That incremental distance is added to the viewpoint pose over
one or more frames. Stepping faster or slower changes speed, but it is not possible to
adjust speed between foot-strikes. Maneuvering is not possible unless the algorithm
includes a sensor-signal threshold so that it ignores small foot movements or light
floor strikes.
Moving the user forward a set distance for each foot-strike generally does not lead
to a relatively constant speed for rhythmic-phase walking even if the total distance
to be moved is spread over several frames. In an exaggerated fashion, Fig.
11.3
shows a speed profile for distance (
a
) added uniformly over several frames and
(
b
) added in a sawtooth pattern in order to avoid multi-frame pauses in the optic
flow occurring when speed goes to zero or near zero between steps. Comparing
these profiles to Fig.
11.1
reveals that neither waveform is a good approximation
of natural walking. Overcoming the limitations of discrete-step based interfaces—
latency, speed variations during rhythmic-phase, inability to maneuver and adjust
steed—requires additional data about the user's stepping motion.
11.2.1.2 Continuously Measuring Leg Position
The addition of trackers to the front or back of the user's legs (or knees, shins, ankles,
or feet) provides a continuous stream of time-stamped tracker data from which the
six events in the walking-in-place cycle can be detected: motion of one leg begins
at foot-off, motion reverses direction when the tracker reaches its maximum extent,
and motion of that leg stops at foot-strike; then similarly for the other leg. Leg speed
can be computed from the tracker data.
