Environmental Engineering Reference
In-Depth Information
removed, performance deteriorated. The results of this work shed light on ways to
minimize the amount of visual feedback necessary for successful control of precision
upper extremity movements in virtual environments. While the rendering of complex
images capable of simulating the full human hand is now possible in VEs, it remains
problematic in two ways. First, the motion capture and computing technology can result
in significant increases in equipment costs to render a realistic hand. More importantly,
the complexity of the rendering process generally results in significant latency problems,
with time lags on the scale of 150+ ms from the movement of the real hand to its
represented movement within the virtual environment (Wang & Popović, 2009). Latencies
of this magnitude can have a significant negative impact on human performance (Ellis et
al., 1997). Further, time delays in the range of 16-33 ms become noticeable to subjects
when performing simple visual tasks in virtual reality (Mania et al., 2004). As a result of
these problems, a key area of research in the development of successful, cost-effective VEs
must relate to simulator validity. That is, the degree of realism the environment provides
in approximating a real situation. Simulator validity has been identified as a key
parameter for the effectiveness of learning in training simulations (Issenberg et al., 2005).
This is extremely important in applications such as neurologic rehabilitation, where the
ultimate goal is to ensure that practice in the virtual environment will carry over to
function in activities of daily living. We must identify the minimal features of sensory
feedback required for valid simulations so that humans can interact in ways sufficiently
similar to movements in natural environments. In their initial study, Mason and
Bernardin (2009) identified some sufficient visual feedback parameters for young adults.
We conducted a follow up study using a similar paradigm to see if these results
generalize to older and younger user groups.
1
In our follow-up study, participants were asked to reach from a designated start position to
grasp and lift a target cube. We manipulated three variables. The first was age group
membership: children (7-12 years), young adults (18-30 years), middle age adults (40-50
years) and senior adults (60+ years). Each of these groups included 12 participants. Second,
we manipulated target distance by placing the target object at either 7.5cm or 15cm from the
start mark. Finally we varied visual feedback of the hand by providing the subject with one
of five visual feedback conditions (Figure 2). In the no vision (NV) condition, the subject was
not given any graphical feedback about the position of the hand. In the full vision crude
(FVC) condition, graphical feedback about hand position (10mm spheres at the fingertips)
was provided throughout the entire reach-to-grasp movement. For the vision up to peak
velocity (VPV) condition, graphical feedback about hand position was extinguished once
peak velocity of the wrist was reached. In the vision until movement onset (VMO)
condition, graphical feedback of the hand was extinguished at the start of movement. For
these conditions, subjects were prevented from seeing the real workspace below the mirror
so that vision of the actual limb and surrounding environment was absent. For the final
condition (full vision or FV), subjects were given full vision of the real hand as in a natural
environment. Computer rendered graphical information about the target size and location
was always available. All visual feedback was presented with visual stimuli of moderate
contrast in relation to the background.
1
A preliminary version of these results were published elsewhere (Grabowski & Mason, 2011).
