无需标记的智能边缘传感器系统实现高效人体步态分析
Marker-free Human Gait Analysis using a Smart Edge Sensor System
- 用多摄像头与边缘计算结合，无标记追踪3D身体姿态
- 通过孪生网络与三元组损失，实现步态模式精准识别
- 适合医疗监测、安防识别等真实场景应用
人体步态是神经与肌肉系统复杂互动的结果，反映个体神经和生理状态，是生物力学和医学专家的重要工具。传统观察法成本低但可靠性差，基于标记的光学系统虽准确却昂贵且耗时。本文提出一种新型无标记步态分析方法，利用多摄像头与智能边缘传感器，无需标识点即可估计三维身体姿态。我们设计了一种孪生嵌入网络，结合三元组损失，通过步态序列映射到嵌入空间，使同一人或同种活动的序列聚集，不同个体则分离。实验表明该系统在多样真实环境中具备高效自动化步态分析潜力，可支持广泛应用场景。
原文摘要 · Abstract (English)
The human gait is a complex interplay between the neuronal and the muscular systems, reflecting an individual's neurological and physiological condition. This makes gait analysis a valuable tool for biomechanics and medical experts. Traditional observational gait analysis is cost-effective but lacks reliability and accuracy, while instrumented gait analysis, particularly using marker-based optical systems, provides accurate data but is expensive and time-consuming. In this paper, we introduce a novel markerless approach for gait analysis using a multi-camera setup with smart edge sensors to estimate 3D body poses without fiducial markers. We propose a Siamese embedding network with triplet loss calculation to identify individuals by their gait pattern. This network effectively maps gait sequences to an embedding space that enables clustering sequences from the same individual or activity closely together while separating those of different ones. Our results demonstrate the potential of the proposed system for efficient automated gait analysis in diverse real-world environments, facilitating a wide range of applications.
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