학술논문

Robust and Interpretable General Movement Assessment Using Fidgety Movement Detection
Document Type
Periodical
Source
IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. Biomedical and Health Informatics, IEEE Journal of. 27(10):5042-5053 Oct, 2023
Subject
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Frequency modulation
Pediatrics
Skeleton
Australia
Hospitals
Cameras
Trajectory
Cerebral palsy
fidgety movements
general movement assessment
infant pose
Language
ISSN
2168-2194
2168-2208
Abstract
Fidgety movements occur in infants between the age of 9 to 20 weeks post-term, and their absence are a strong indicator that an infant has cerebral palsy. Prechtl's General Movement Assessment method evaluates whether an infant has fidgety movements, but requires a trained expert to conduct it. Timely evaluation facilitates early interventions, and thus computer-based methods have been developed to aid domain experts. However, current solutions rely on complex models or high-dimensional representations of the data, which hinder their interpretability and generalization ability. To address that we propose $\text {FidgetyFind}$, a method that detects fidgety movements and uses them towards an assessment of the quality of an infant's general movements. $\text {FidgetyFind}$ is true to the domain expert process, more accurate, and highly interpretable due to its fine-grained scoring system. The main idea behind $\text {FidgetyFind}$ is to specify signal properties of fidgety movements that are measurable and quantifiable. In particular, we measure the movement direction variability of joints of interest, for movements of small amplitude in short video segments. $\text {FidgetyFind}$ also comprises a strategy to reduce those measurements to a single score that quantifies the quality of an infant's general movements; the strategy is a direct translation of the qualitative procedure domain experts use to assess infants. This brings $\text {FidgetyFind}$ closer to the process a domain expert applies to decide whether an infant produced enough fidgety movements. We evaluated $\text {FidgetyFind}$ on the largest clinical dataset reported, where it showed to be interpretable and more accurate than many methods published to date.