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Media Adaptation in Biofeedback System |
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In this project, we present a media adaptation
framework for an immersive biofeedback system
for stroke patient rehabilitation. In our
biofeedback system, media adaptation refers to
changes in audio/visual feedback as well as
changes in physical environment. Effective media
adaptation frameworks help patients recover
generative plans for arm movement with potential
for significantly shortened therapeutic time.
The media adaptation problem has significant
challenges – (a) high dimensionality of
adaptation parameter space (b) variability in
the patient performance across and within
sessions (c) the actual rehabilitation plan is
typically a non first-order Markov process,
making the learning task hard.
Our key insight is to understand media
adaptation as a real-time feedback control
problem. We use a mixture-of-experts based
Dynamic Decision Network (DDN) for online media
adaptation. We train DDN mixtures per patient,
per session. The mixture models address two
basic questions – (a) given a specific
adaptation suggested by the domain expert,
predict patient performance and (b) given an
expected performance, determine optimal
adaptation decision. The questions are answered
through an optimality criterion based search on
DDN models trained in previous sessions. We have
also developed new validation metrics and have
very good results for both questions on actual
stroke rehabilitation data. | |
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Biofeedback |
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The
goal of this project is to design a real time
multimodal biofeedback system for stroke patient
rehabilitation. The problem is important as
traditional mechanisms of rehabilitation are
monotonous and do not incorporate detailed
quantitative assessment of recovery in addition
to traditional clinical schemes. We have been
working on developing an experiential media
system that integrates task dependent physical
therapy and cognitive stimuli within an
interactive, multimodal environment. The
environment provides a purposeful, engaging,
visual and auditory scene in which patients can
practice functional therapeutic reaching tasks
while receiving different types of simultaneous
feedback indicating measures of both performance
and results. In this project, I focus on three
parts: (a) real-time motion analysis, (b) new
metrics to validate the ability of the system to
promote learnability, stylization and engagement
and (c) human-computer joint multimodal
adaptation. | |
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Linear Transform Approximation |
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This project aims to develop a novel framework
to systematically trade-off computational
complexity with output distortion in linear
multimedia transforms, in an optimal manner. The
problem is important in real-time systems where
the computational resources available are
time-dependent. We solve the real-time
adaptation problem by developing an approximate
transform framework. There are three key
contributions of this paper – (a) a fast basis
approximation framework that allows us to store
signal independent partial transform results to
be used in real-time, (b) estimating the
complexity distortion curve for the linear
transform using a basis set approximation and
(c) determining optimal operating points and a
meta-data embedding algorithm for images that
allows for real-time adaptation. We have applied
this approach on the FFT and DCT transform using
Haar wavelet basis. Our results validate our
theoretical approach by showing that we can
reduce transform computational complexity
significantly while minimizing distortion.
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Shape Complexity |
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This project deals with the problem of
estimating 2D shape complexity. This has
important applications in computer vision as
well as in developing efficient shape
classification algorithms. We define shape
complexity using correlates of Kolmogorov
complexity – entropy measures of global distance
and local angle, and a measure of shape
randomness. We tested our algorithm on synthetic
and real world datasets with excellent results.
We also conducted user studies that indicate
that our measure is highly correlated with human
perception. They also reveal an intuitive shape
sensitivity curve – simple shapes are easily
distinguished by small complexity variations,
while complex shapes require significant
complexity differences to be differentiated.
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Effort in Human Pose |
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This project deals with the problem of
estimating the effort required to maintain a
static pose by human beings. The problem is
important in developing dance summarization and
rehabilitation applications. We estimate the
human pose effort using two kinds of body
constraints – skeletal constraints and
gravitational constraints. The extracted
features are combined together using SVM
regression to estimate the pose effort. We
tested our algorithm on 55 dance poses with
different annotated efforts with excellent
results. Our user studies additionally validate
our approach.
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Approximate
Pattern Classification |
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In this project, we present an efficient 3D
shape rejection algorithm for unlabeled 3D
markers. The problem is important in domains
such as rehabilitation and the performing arts.
There are three key innovations in our approach
– (a) a multi-resolution shape representation
using Haar wavelets for unlabeled markers, (b) a
multi-resolution shape metric and (c) a shape
rejection algorithm that is predicated on the
simple idea that we do not need to compute the
entire distance to conclude that two shapes are
dissimilar. We tested the approach on a
real-world pose classification problem with
excellent results. We achieved a classification
accuracy of 98% with an order of magnitude
improvement in terms of computational complexity
over a baseline shape matching algorithm.
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