Hardware Design Optimization for Human Motion Tracking Systems
A stochastic framework for evaluating and comparing the expected performance of sensing systems for interactive computer graphics
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This research introduces a stochastic framework forevaluating and comparing the expected performance ofsensing systems for interactive computer graphics.Incorporating models of the sensor devices andexpected user motion dynamics, this framework enablescomplementary system- and measurement-level hardwareinformation optimization, independent of algorithmand motion paths. The approach for system-leveloptimization is to estimate the asymptotic positionand/or orientation uncertainty at many pointsthroughout a desired working volume or surface, andto visualize the results graphically. This globalper...
This research introduces a stochastic framework for
evaluating and comparing the expected performance of
sensing systems for interactive computer graphics.
Incorporating models of the sensor devices and
expected user motion dynamics, this framework enables
complementary system- and measurement-level hardware
information optimization, independent of algorithm
and motion paths. The approach for system-level
optimization is to estimate the asymptotic position
and/or orientation uncertainty at many points
throughout a desired working volume or surface, and
to visualize the results graphically. This global
performance estimation can provide both a
quantitative assessment of the expected performance
and intuition about how to improve the type and
arrangement of sources and sensors, in the context of
the desired working volume and expected scene
dynamics. Using the same model components required
for these system-level optimization, the optimal
sensor sampling time can be determined with respect
to the expected scene dynamics for measurement-level
optimization.
evaluating and comparing the expected performance of
sensing systems for interactive computer graphics.
Incorporating models of the sensor devices and
expected user motion dynamics, this framework enables
complementary system- and measurement-level hardware
information optimization, independent of algorithm
and motion paths. The approach for system-level
optimization is to estimate the asymptotic position
and/or orientation uncertainty at many points
throughout a desired working volume or surface, and
to visualize the results graphically. This global
performance estimation can provide both a
quantitative assessment of the expected performance
and intuition about how to improve the type and
arrangement of sources and sensors, in the context of
the desired working volume and expected scene
dynamics. Using the same model components required
for these system-level optimization, the optimal
sensor sampling time can be determined with respect
to the expected scene dynamics for measurement-level
optimization.
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