SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition
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 dependenc...
| Authors | Deepika Gurung, Lala Shakti Swarup Ray, Mengxi Liu et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.