SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition
This addresses the need for efficient, real-time, and private activity recognition on edge devices, representing an incremental improvement through co-designed architecture.
The paper tackles the problem of sensor-based human activity recognition under edge deployment constraints by proposing SPECTRA, a spectral-temporal neural network that matches or approaches larger CNN-LSTM and Transformer baselines across five datasets while substantially reducing parameters, latency, and energy.
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.