Green Wearable Computing Physics

核心概念

• 脉冲神经网络(spiking neural network)
• SNN(snn)
• 物理感知(physics-aware)
• 人体活动识别(human activity recognition)

核心贡献

1. To bridge this gap, we propose the Physics-Aware Spiking Neural Network (PAS-Net), a fully multiplier-free architecture explicitly tailored for Green HAR.
2. 详见论文原文
3. 详见论文原文

技术方法

• 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

应用领域

• 可穿戴设备活动识别
• 人体活动识别

实现要点

关键组件

• 数据预处理管道
• 神经网络架构设计
• 训练策略与优化
• 评估指标与验证

技术挑战

• To bridge this gap, we propose the Physics-Aware Spiking Neural Network (PAS-Net), a fully multiplier-free architecture explicitly tailored for Green HAR.
• 详见论文讨论部分

实验结果

Evaluated across seven diverse datasets, PAS-Net achieves state-of-the-art accuracy while replacing dense operations with sparse 0.1 pJ integer accumulations.

代码示例

# 核心架构示例

# 脉冲神经网络示例
import torch
import torch.nn as nn

class SNNLayer(nn.Module):
    def __init__(self, input_size, hidden_size):
        super().__init__()
        self.fc = nn.Linear(input_size, hidden_size)
        self.threshold = 1.0
        
    def forward(self, x):
        # 前向传播与脉冲生成
        mem = self.fc(x)
        spike = (mem > self.threshold).float()
        return spike

限制与展望

• 当前方法的主要限制
• 未来研究方向
• 潜在改进空间

参考文献

• Naichuan Zheng et al. (2026). "Towards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-based Human Activity Recognition." arXiv:2604.10458v1.

激活关键词

• 物理感知
• spiking
• physics-aware
• snn

技能自动生成于: 2026-04-15 来源: arXiv自动化研究工作流