Scaling Dynamic Mode Decomposition For Real Time Analysis Of Infant Movements

Navya Annapareddy, Lisa Letzkus, Santina Zanelli, Stephen Baek

Track: Short paper

Keywords: Computer Vision, Digital Twin, Healthcare, Machine Learning, Pose Estimation, Dynamical Systems, ODE

TL;DR: We apply dynamic mode decomposition (DMD) as novel way to analyze characteristic motions in infant motion data in a more interpretable, efficient, and scalable manner than existing methods.

Abstract:

The analysis of characteristic motions in infants plays a pivotal role in quantifying developmental progress and clinical risk for neurodevelopmental and musculoskeletal abnormalities. Traditional methods often rely on resource intensive
manual motion assessments carried out by clinicians while computer assisted approaches frequently utilize computationally expensive simulations or black-box
classification models. These approaches struggle to efficiently to both capture
and differentiate the highly correlated dynamics of infant motion, limiting their
ability to deliver actionable insights in a clinically viable decision time frame. In
response to these challenges, we introduce the use of Dynamic Mode Decomposition (DMD) as a transformative approach for decomposing complex infant motion
into interpretable, independent components that are linearly additive in nature.
DMD not only enables extraction of large scale clinically meaningful patterns
but also can integrate with existing computer assisted interventions with regard
to standardized motion features. We assess an optimized DMD formulation on
275,000 frames of infant motion in clinical settings that have undergone manual
motion assessment by clinicians. Our experimental results show that using DMD
modes as predictive components not only result in equal or superior accuracy in
predicting abnormal clinical motion assessments compared to traditional manual
or computer assisted methods but serve as highly data rich features themselves
that can be used as a novel basis for personalized clinical analysis and uncertainty
quantification at scale.

Presenter: ~Navya_Annapareddy1

Submission Number: 21