用混合卷积与自编码器分层融合多传感器动作数据，准确率超现有方法3%。
CNN Autoencoders for Hierarchical Feature Extraction and Fusion in Multi-sensor Human Activity Recognition
- 构建三阶段分层结构：局部、全局特征提取与融合，提升多传感器数据利用效率。
- 在UCI-HAR、DaLiAc和帕金森步态数据集上分别达97%、97%、88%准确率。
- 最优配置为每层8个卷积层，首块使用256核过完备自编码器，末块64核适配分类器。
深度学习广泛应用于基于安装于人体各部位的惯性测量单元（IMUs）信号的人体活动识别（HAR）。此类任务面临的主要挑战是多源IMU数据的分析。本文提出一种分层无监督融合（HUF）模型，通过卷积神经网络（CNN）与自编码器（AE）的混合结构实现对IMU数据的特征提取与融合。首先设计堆叠的CNN-AE将短时信号嵌入高维特征；其次开发另一CNN-AE网络对每个传感器单元的特征进行局部融合；最后通过第三层CNN-AE实现全局特征统一，生成唯一特征集。实验分析了模型超参数影响，最佳表现对应每层8个卷积层。首阶段采用含256个核的过完备自编码器提取特征，末阶段缩减至64核以适配分类器输入。该模型在UCI-HAR、DaLiAc和帕金森病步态数据集上分别取得97%、97%、88%的分类准确率，较当前最优监督方法提升近3%。
原文摘要 · Abstract (English)
Deep learning methods have been widely used for Human Activity Recognition (HAR) using recorded signals from Iner-tial Measurement Units (IMUs) sensors that are installed on various parts of the human body. For this type of HAR, sev-eral challenges exist, the most significant of which is the analysis of multivarious IMU sensors data. Here, we introduce a Hierarchically Unsupervised Fusion (HUF) model designed to extract, and fuse features from IMU sensors data via a hybrid structure of Convolutional Neural Networks (CNN)s and Autoencoders (AE)s. First, we design a stack CNN-AE to embed short-time signals into sets of high dimensional features. Second, we develop another CNN-AE network to locally fuse the extracted features from each sensor unit. Finally, we unify all the sensor features through a third CNN-AE architecture as globally feature fusion to create a unique feature set. Additionally, we analyze the effects of varying the model hyperparameters. The best results are achieved with eight convolutional layers in each AE. Furthermore, it is determined that an overcomplete AE with 256 kernels in the code layer is suitable for feature extraction in the first block of the proposed HUF model; this number reduces to 64 in the last block of the model to customize the size of the applied features to the classifier. The tuned model is applied to the UCI-HAR, DaLiAc, and Parkinson's disease gait da-tasets, achieving the classification accuracies of 97%, 97%, and 88%, respectively, which are nearly 3% better com-pared to the state-of-the-art supervised methods.
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