[2603.04477] Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN

Computer Science > Machine Learning arXiv:2603.04477 (cs) [Submitted on 4 Mar 2026] Title:Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN Authors:Yanhua Zhao View a PDF of the paper titled Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN, by Yanhua Zhao View PDF HTML (experimental) Abstract:Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive means of monitoring human gait and posture. We present an activity classification system based on a circular dilated convolutional neural network (CDCNN) that processes multi-modal time-series data from such insoles. The model operates on 160-frame windows with 24 channels (18 pressure, 3 accelerometer, 3 gyroscope axes), achieving 86.42% test accuracy in a subject-independent evaluation on a four-class task (Standing, Walking, Sitting, Tandem), compared with 87.83% for an extreme gradient-boosted tree (XGBoost) model trained on flattened data. Permutation feature importance reveals that inertial sensors (accelerometer and gyroscope) contribute substantially to discrimination. The approach is suitable for embedded deployment and real-time inference. Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2603.04477 [cs.LG]   (or arXiv:2603.04477v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2603.04477 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Sub...