标题： 基于双输入Transformer的网络从单个RGB视频中进行定量步态分析 显示英文标题

标题： Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network

摘要： 步态和运动分析已成为诊断健康状况、监测广泛疾病谱的疾病进展，并实施和评估治疗、手术和康复干预的成熟临床工具。 然而，定量运动评估仍受限于昂贵的动作捕捉系统和专业人员，限制了其可及性和更广泛的应用。 深度神经网络的最新进展使得通过单摄像头视频进行定量运动分析成为可能，为传统动作捕捉系统提供了一种可及的替代方案。 在本文中，我们提出了一种通过双模式输入卷积Transformer网络进行临床步态分析的高效方法。 所提出的系统利用双输入Transformer模型，从单视角摄像头拍摄的单张RGB视频中估计关键步态参数。 该系统在估计关键指标（如步态偏差指数（GDI）、膝关节屈曲角度、步长和步行频率）方面表现出高准确性，并在患有运动障碍的个体数据集上进行了验证。 值得注意的是，我们的方法在各种场景中超越了最先进的方法，使用更少的资源，并证明在临床应用中非常合适，尤其是在资源有限的环境中。 显示英文摘要

摘要： 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.