用环形空洞卷积分析智能鞋垫数据，实现高精度动作识别。
Activity Recognition from Smart Insole Sensor Data Using a Circular Dilated CNN
- 设计环形空洞卷积网络，处理多模态时间序列数据
- 在四类动作上达到86.42%准确率，接近XGBoost表现
- 适合嵌入式部署，适用于实时步态监测
配备压力传感器、加速度计和陀螺仪的智能鞋垫可非侵入式监测人体步态与姿势。本文提出一种基于环形空洞卷积神经网络（CDCNN）的动作分类系统，处理来自此类鞋垫的多模态时间序列数据。模型以160帧窗口为输入，包含24个通道（18个压力、3个加速度计轴、3个陀螺仪轴），在独立于受试者的四类任务（站立、行走、坐下、单脚站立）中测试准确率达86.42%，略低于使用展平数据训练的极端梯度提升树（XGBoost）模型的87.83%。置换特征重要性分析显示，惯性传感器（加速度计与陀螺仪）对分类贡献显著。该方法适合嵌入式部署与实时推理。
原文摘要 · Abstract (English)
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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