Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
Accurate diagnosis of gait impairments is often hindered by subjective or
costly assessment methods, with current solutions requiring either expensive
multi-camera equipment or relying on subjective clinical observation. There is
a critical need for accessible, objective tools that can aid in gait assessment
while preserving patient privacy. In this work, we present a mobile
phone-based, privacy-preserving artificial intelligence (AI) system for
classifying gait impairments and introduce a novel dataset of 743 videos
capturing seven distinct gait patterns. The dataset consists of frontal and
sagittal views of trained subjects simulating normal gait and six types of
pathological gait (circumduction, Trendelenburg, antalgic, crouch,
Parkinsonian, and vaulting), recorded using standard mobile phone cameras. Our
system achieved 86.5% accuracy using combined frontal and sagittal views, with
sagittal views generally outperforming frontal views except for specific gait
patterns like Circumduction. Model feature importance analysis revealed that
frequency-domain features and entropy measures were critical for classifcation
performance, specifically lower limb keypoints proved most important for
classification, aligning with clinical understanding of gait assessment. These
findings demonstrate that mobile phone-based systems can effectively classify
diverse gait patterns while preserving privacy through on-device processing.
The high accuracy achieved using simulated gait data suggests their potential
for rapid prototyping of gait analysis systems, though clinical validation with
patient data remains necessary. This work represents a significant step toward
accessible, objective gait assessment tools for clinical, community, and
tele-rehabilitation settings
