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Rishad A. Shafik

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Open access Jul 2026

Sensor-Modality-Aware Human Activity Recognition with the Convolutional Tsetlin Machine: Interpretable and Resource-Efficient Neuro-Symbolic Learning

This work investigates the Convolutional Tsetlin Machine for multimodal HAR using only the raw inertial signals of the UCI-HAR dataset, rather than its pre-computed 561-feature representation, to address predictive performance, interpretability and suitability for embedded and mobile platforms.

O. Tarasyuk, A. Gorbenko, O. Gordieiev et al. · 0 citations