[2603.26482] SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition

Computer Science > Machine Learning arXiv:2603.26482 (cs) [Submitted on 27 Mar 2026] Title:SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition Authors:Deepika Gurung, Lala Shakti Swarup Ray, Mengxi Liu, Bo Zhou, Paul Lukowicz View a PDF of the paper titled SPECTRA: An Efficient Spectral-Informed Neural Network for Sensor-Based Activity Recognition, by Deepika Gurung and 4 other authors View PDF HTML (experimental) Abstract: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...