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A Shape-Based Functional Index for Objective Assessment of Pediatric Motor Function

Shashwat Kumar, Arafat Rahman, Robert Gutierrez, Sarah Livermon, Allison N. McCrady, Silvia Blemker, Rebecca Scharf, Anuj Srivastava, Laura E. Barnes

TL;DR

A novel method using wearable sensors to objectively assess motor function during daily activities in patients with DMD, 9 with SMA, and 13 age-matched controls is introduced, proposing a novel motor function index.

Abstract

Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders.

A Shape-Based Functional Index for Objective Assessment of Pediatric Motor Function

TL;DR

A novel method using wearable sensors to objectively assess motor function during daily activities in patients with DMD, 9 with SMA, and 13 age-matched controls is introduced, proposing a novel motor function index.

Abstract

Clinical assessments for neuromuscular disorders, such as Spinal Muscular Atrophy (SMA) and Duchenne Muscular Dystrophy (DMD), continue to rely on subjective measures to monitor treatment response and disease progression. We introduce a novel method using wearable sensors to objectively assess motor function during daily activities in 19 patients with DMD, 9 with SMA, and 13 age-matched controls. Pediatric movement data is complex due to confounding factors such as limb length variations in growing children and variability in movement speed. Our approach uses Shape-based Principal Component Analysis to align movement trajectories and identify distinct kinematic patterns, including variations in motion speed and asymmetry. Both DMD and SMA cohorts have individuals with motor function on par with healthy controls. Notably, patients with SMA showed greater activation of the motion asymmetry pattern. We further combined projections on these principal components with partial least squares (PLS) to identify a covariation mode with a canonical correlation of r = 0.78 (95% CI: [0.34, 0.94]) with muscle fat infiltration, the Brooke score (a motor function score), and age-related degenerative changes, proposing a novel motor function index. This data-driven method can be deployed in home settings, enabling better longitudinal tracking of treatment efficacy for children with neuromuscular disorders.
Paper Structure (21 sections, 5 equations, 11 figures, 3 tables)

This paper contains 21 sections, 5 equations, 11 figures, 3 tables.

Table of Contents

  1. Abstract
  2. Introduction
  3. Materials and Methods
  4. Results
  5. Discussion and Future Work
  6. Acknowledgments
  7. Data Availability
  8. Code Availability
  9. Author Contributions
  10. Competing Interests

Figures (11)

  • Figure 1: Overview of the study and the proposed shape analysis pipeline. Wearable sensors capture IMU signals from participants performing activities of daily living. This data is combined with shape analysis and external assessments to develop a canonical index of motor function.
  • Figure 2: A simulated illustration of the alignment of arm curls. (a) An example of an arm curl. (b) Temporal rate or warping function of this arm curl. (c) An example of misaligned arm curls. (d) Functions after alignment.
  • Figure 3: Results on performing curve registration and Fréchet mean calculation with temporal matching. (a) Signals with only amplitude variability, (b) Warping functions, (c) Signals with amplitude and phase variability, (d) Signals after registration, (e) Reconstructed warping functions, (f) Euclidean and Shape mean. Note how the shape mean (blue) captures the symmetric shape better than the Euclidean mean (red).
  • Figure 4: (a-d) Results on performing phase amplitude separation on healthy and (e-h) DMD/SMA cohorts.
  • Figure 5: (a-c) Vertical modes of variation obtained from Shape PCA on the curl data. (a) The first mode represents scaling, (b) the second asymmetry in motion while (c) the last represents noise. (d-f) Modes of variation obtained from knocking data. (d) The first mode represents scaling. (e) The second mode represents asymmetry in motion while (f) the last represents sensor noise.
  • ...and 6 more figures