基于视觉的低功耗边缘平台实时跌倒检测

摘要

跌倒检测对于老年人护理和智能监控至关重要，然而，现有的基于视觉的方法主要将其视为静态姿态分类或离散时间模式匹配，从根本上忽略了人体支撑系统的不稳定性动态。本文提出了一种物理信息驱动的跌倒检测框架，将跌倒重新定义为耦合动力系统中的稳定性丧失事件。我们引入了一种新颖的双LTC架构，包括质心子系统和支撑基础子系统，两者均实例化为液态时间常数神经网络，通过自适应时间常数连续建模惯性轨迹演化和地面接触调整，实现跌倒运动的物理可解释性。一个可学习的耦合模块模拟两个子系统之间的物理相互作用，而稳定性流形分类器在联合潜在空间中运行，通过李雅普诺夫启发的稳定性指标检测边界穿越。互补的反事实轨迹投影和碰撞时间估计进一步支持不可逆性评估和早期预警。该架构旨在支持三状态预测范式（正常、跌倒中、已跌倒）；在本初步研究中，我们验证了基于二分类数据集（正常与跌倒中）的核心稳定性判别能力，将完整的三状态时间过渡留待未来工作。与传统CNN-RNN流水线不同，所提出的公式编码了连续时间机械惯性，生成了一个参数低于5万的网络，能够在资源受限的边缘设备上实现实时推理。大量实验证明了其具有竞争力的准确性和优越的物理可解释性，验证了其在低计算量视觉跌倒检测中的有效性。

English

Falling detection is vital for elderly care and intelligent surveillance; however, prevailing vision-based approaches predominantly frame it as static pose classification or discrete temporal pattern matching, fundamentally overlooking the instability dynamics of the human support system. This paper proposes a physics-informed falling detection framework that recasts falling as a stability-loss event in a coupled dynamical system. We introduce a novel dual-LTC architecture comprising a Center-of-Mass (CoM) subsystem and a Base-of-Support (BoS) subsystem, both instantiated as Liquid Time-Constant (LTC) neural networks to continuously model inertial trajectory evolution and ground-contact adjustment through adaptive time constants, Physical interpretability of falling motion. A learnable coupling module emulates physical interaction between the two subsystems, while a Stability Manifold classifier operates in the joint latent space to detect boundary crossing via Lyapunov-inspired stability metrics. Complementary counterfactual trajectory projection and Time-to-Collision (TTC) estimation further enable irreversibility assessment and early warning. The architecture is designed to support a three-state prediction paradigm (Normal, Falling, Fallen); in this preliminary study, we validate the core stability discrimination capability on a two-class dataset (Normal vs. Falling), leaving the full three-state temporal transition to future work. Unlike conventional CNN--RNN pipelines, the proposed formulation encodes continuous-time mechanical inertia, yielding a sub-50K-parameter network capable of real-time inference on resource-constrained edge devices. Extensive experiments demonstrate competitive accuracy with superior physical interpretability, validating its efficacy for low-compute visual fall detection.