Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
This work provides an incremental activity recognition system for individuals using smart insoles, suitable for embedded deployment.
This paper developed a circular dilated convolutional neural network (CDCNN) to classify four human activities (Standing, Walking, Sitting, Tandem) using smart insole sensor data. The CDCNN achieved 86.42% test accuracy, slightly lower than an XGBoost model's 87.83%.
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.