Biomedical Engineering Reference
In-Depth Information
either a minified view or a normal view across different conditions. The goal was to
make sure the multiplexing device did not cause individuals to overestimate collision
risks during active walking or passive viewing. The perceived passable space around
the obstacle and variability of collision judgments were both greater for patients than
for normally sighted participants during simulated walking (i.e., passive viewing),
absent the minified device. The collision judgments were also more accurate for the
normally sighted controls during the walking condition. Consequently, the minified
device had no effect on the patients with PFL or the controls during either condition.
These findings indicate that while the multiplexing device did not degrade perfor-
mance in either population—an important finding given the increased attentional
demands imposed by the device—it also did not improve perceptual judgments of
collisions in the virtual environment.
These two experiments demonstrate the advantages of VR-based assessment of
patients suffering from visual disorders. Specifically, important research questions
about obstacle avoidance can be investigated without risk of injury to patients. In
addition, VR enables simulation environments that mimic pathological deficits in
healthy participants. This helps to ease the burden of participation by the clinical
populations while researchers can draw from a large participant pool. While more
research is necessary to ensure the viability of approaches such as these, these two
experiments provide a solid foundation for exploring similar types of questions.
15.2 Dynamical Disease and VR-Based Assessment
Up to this point we have reviewed research associated with new developments in
rehabilitation science sparked by interactive, immersive virtual environments. Over
the last 30years, clinical assessment has been undergoing another, equally important
shift in thinking—the emergence of the concept of
dynamical disease
and techniques
to measure it (see Van Orden [
61
], and West [
76
], for reviews). Dynamical disease,
broadly defined, involves a physiological control system operating within parameter
ranges that constrain the system's dynamics in such a way that it generates patholog-
ical behavior [
16
,
34
]. This shift challenges the premise that behavioral variability
is adverse to healthy functioning—a prominent assumption in clinical locomotor
research (e.g., [
4
,
19
,
39
,
52
,
64
,
73
]). A central tenet of this approach is that the
system's dynamics, indexed by continuous measurement of locomotor patterns, are
more revealing than classic summary statistics alone. For example, healthy adult gait
exhibits a movement signature that is altered by neurological insult due to injury,
aging, or disease [
20
,
54
]; a difference that is not adequately captured by themean and
variance of behavior. The question of how one should measure the system dynamics,
specifically how to quantify the patterns of variability in gait measures, is now at the
forefront of clinical assessment research.
Virtual reality has the potential to play an important role in this transformation, for
it enables the control of information that could influence the dynamics of movement
[
66
]. This offers the flexibility to manipulate visual stimuli during walking in an
