Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
Year: 2026
Type: article
Abstract: Smart insoles equipped with pressure sensors, accelerometers, and gyroscopes offer a non-intrusive way 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 timeseries 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). Permutation feature importance reveals the distribution of contributions made by various sensors to the classification process. The proposed approach has potential applications in areas such as smart insole design, health monitoring, and rehabilitation.
Author: Yanhua Zhao
Institution: Technische Universität Darmstadt
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7134044871
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