基于智能鞋垫传感器数据的活动识别使用圆形扩张卷积神经网络
Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
AI总结：
本文提出了一种基于圆形扩张卷积神经网络的活动识别方法，利用多模态时间序列数据实现高准确率的活动分类。
摘要：
配备压力传感器、加速度计和陀螺仪的智能鞋垫提供了一种非侵入性的人体步态和姿势监测方法。我们提出了一种基于圆形扩张卷积神经网络（CDCNN）的活动分类系统，该系统处理此类鞋垫的多模态时间序列数据。模型在160帧窗口上运行，有24个通道（18个压力、3个加速度计、3个陀螺仪轴），在四类任务（站立、行走、坐立、并行）上进行无主体评估时，测试准确率为86.42%，比在扁平化数据上训练的极端梯度提升树（XGBoost）模型的87.83%稍低。排列特征重要性揭示了惯性传感器（加速度计和陀螺仪）在区分中的显著贡献。该方法适用于嵌入式部署和实时推断。
英文摘要：
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.
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