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

TL;DR

This paper introduces a circular dilated CNN for activity recognition using multi-modal data from smart insoles, achieving high accuracy and suitable for real-time embedded applications.

Contribution

The novel CDCNN architecture effectively processes multi-modal insole data for activity classification, demonstrating competitive accuracy and real-time applicability.

Findings

Achieved 86.42% accuracy on a four-class activity dataset.

Inertial sensors significantly improve classification performance.

Model is suitable for embedded and real-time deployment.

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.