Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network
This work addresses the need for affordable and accessible clinical gait analysis, particularly in resource-constrained environments, by replacing costly motion capture systems with a video-based approach.
The paper tackled the problem of making quantitative gait analysis more accessible by developing a dual-input Transformer network that estimates gait parameters from single RGB videos, achieving high accuracy in metrics like gait deviation index and knee flexion angle and surpassing state-of-the-art methods with fewer resources.
Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly motion capture systems and specialized personnel, restricting its accessibility and broader application. Recent advancements in deep neural networks have enabled quantitative movement analysis using single-camera videos, offering an accessible alternative to conventional motion capture systems. In this paper, we present an efficient approach for clinical gait analysis through a dual-pattern input convolutional Transformer network. The proposed system leverages a dual-input Transformer model to estimate essential gait parameters from single RGB videos captured by a single-view camera. The system demonstrates high accuracy in estimating critical metrics such as the gait deviation index (GDI), knee flexion angle, step length, and walking cadence, validated on a dataset of individuals with movement disorders. Notably, our approach surpasses state-of-the-art methods in various scenarios, using fewer resources and proving highly suitable for clinical application, particularly in resource-constrained environments.