Wearable IMU-based Human Activity Recognition (HAR) relies heavily on Deep Neural Networks (DNNs), which are burdened by immense computational and buffering demands. Their power-hungry floating-point operations and rigid requirement to process complete temporal windows severely cripple battery-constrained edge devices. While Spiking Neural Networks (SNNs) offer extreme event-driven energy efficiency, standard architectures struggle with complex biomechanical topologies and temporal gradient degradation. To bridge this gap, we propose the Physics-Aware Spiking Neural Network (PAS-Net), a fully multiplier-free architecture explicitly tailored for Green HAR. Spatially, an adaptive symmetric topology mixer enforces human-joint physical constraints. Temporally, an $O(1)$-memory causal neuromodulator yields context-aware dynamic threshold neurons, adapting actively to non-stationary movement rhythms. Furthermore, we leverage a temporal spike error objective to unlock a flexible early-exit mechanism for continuous IMU streams. Evaluated across seven diverse datasets, PAS-Net achieves state-of-the-art accuracy while replacing dense operations with sparse 0.1 pJ integer accumulations. Crucially, its confidence-driven early-exit capability drastically reduces dynamic energy consumption by up to 98\%. PAS-Net establishes a robust, ultra-low-power neuromorphic standard for always-on wearable sensing. The source code and pre-trained models are publicly available at https://github.com/zhengnaichuan2022/PAS-Net.git.

翻译：基于可穿戴IMU的人体活动识别（HAR）严重依赖深度神经网络（DNN），这类网络受困于巨大的计算与缓冲需求。其高能耗的浮点运算以及对完整时间窗口的刚性处理要求，严重制约了电池受限的边缘设备。尽管脉冲神经网络（SNN）具备极致的事件驱动能效优势，但标准架构在处理复杂生物力学拓扑结构与时间梯度退化问题时仍面临挑战。为弥补这一差距，我们提出物理感知脉冲神经网络（PAS-Net），一种专门为绿色HAR设计的完全无乘法器架构。在空间维度上，自适应对称拓扑混合器强制执行人体关节物理约束；在时间维度上，一种$O(1)$内存的因果神经调节器生成具有上下文感知能力的动态阈值神经元，主动适应非平稳运动节律。此外，我们利用时间脉冲误差目标实现连续IMU流的灵活早期退出机制。经七个不同数据集评估，PAS-Net在实现最先进精度的同时，将密集运算替换为稀疏的0.1 pJ整数累加。关键在于，其置信度驱动的早期退出能力可将动态能耗大幅降低高达98%。PAS-Net为始终在线的可穿戴感知建立了稳健、超低功耗的神经形态标准。源代码与预训练模型已公开于https://github.com/zhengnaichuan2022/PAS-Net.git。