Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
#activity recognition #smart insole #CNN #sensor data #wearable #deep learning #health monitoring
📌 Key Takeaways
- Researchers developed a circular dilated CNN model for activity recognition using smart insole sensor data.
- The model enhances accuracy in identifying human activities by processing spatial-temporal data from insoles.
- Circular dilated convolutions improve feature extraction across sensor arrays, capturing complex movement patterns.
- This approach has potential applications in healthcare, sports analytics, and wearable technology.
📖 Full Retelling
arXiv:2603.04477v1 Announce Type: cross
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 accu
🏷️ Themes
AI in Healthcare, Wearable Technology
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Original Source
--> Computer Science > Machine Learning arXiv:2603.04477 [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 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 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 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: 4 pages, 5 figures 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) Submission history From: Yanhua Zhao [ view email ] [v1] Wed, 4 Mar 2026 15:27:14 UTC (1,281 KB) Full-text links: Access Paper: 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 TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2026-03 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loa...
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