SPECTRA用频谱信息提升传感器动作识别，轻量高效适合边缘设备
SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition
- 融合STFT、深度可分离卷积与通道自注意力，捕捉时频依赖关系
- 参数量减少70%以上，延迟低于15ms，能耗显著降低
- 可在手机和微控制器上实时运行，适合隐私敏感的边缘应用
可穿戴计算中的实时传感器应用需要边缘部署模型以实现低延迟、隐私保护和高效交互。典型例子是基于传感器的人体活动识别（HAR），要求在严格资源约束下兼顾准确率。现有许多深度学习方法将时间传感器信号视为黑箱序列，忽略其频谱-时间结构，且计算开销大。本文提出SPECTRA，一种面向部署的协同设计架构，整合短时傅里叶变换（STFT）特征提取、深度可分离卷积和通道自注意力，以在真实边缘运行时延与内存约束下捕捉频谱-时间依赖。一个紧凑的双向GRU结合注意力池化，在低开销下总结窗口内动态，减轻下游模型负担并保持精度。在五个公开的HAR数据集上，SPECTRA达到或接近更大规模的CNN、LSTM和Transformer基线性能，同时显著降低参数量、延迟和能耗。在谷歌Pixel 9手机和STM32L4微控制器上的部署进一步验证了其端到端可部署性、实时性、隐私性和高效性。
原文摘要 · Abstract (English)
Real time sensor based applications in pervasive computing require edge deployable models to ensure low latency privacy and efficient interaction. A prime example is sensor based human activity recognition where models must balance accuracy with stringent resource constraints. Yet many deep learning approaches treat temporal sensor signals as black box sequences overlooking spectral temporal structure while demanding excessive computation. We present SPECTRA a deployment first co designed spectral temporal architecture that integrates short time Fourier transform STFT feature extraction depthwise separable convolutions and channel wise self attention to capture spectral temporal dependencies under real edge runtime and memory constraints. A compact bidirectional GRU with attention pooling summarizes within window dynamics at low cost reducing downstream model burden while preserving accuracy. Across five public HAR datasets SPECTRA matches or approaches larger CNN LSTM and Transformer baselines while substantially reducing parameters latency and energy. Deployments on a Google Pixel 9 smartphone and an STM32L4 microcontroller further demonstrate end to end deployable realtime private and efficient HAR.
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