对比三类模型在5个数据集上的人体动作识别表现，发现CNN最优。
Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition
- 用经典、深度和生成模型在5个数据集上横向评测
- CNN在所有数据集表现最佳，尤其在Berkeley MHAD上领先
- 随机森林适合小数据，RBM在特征学习上有潜力
随着传感器设备和大规模数据集的普及，人体动作识别（HAR）变得日益重要。本文在五个关键基准数据集（UCI-HAR、OPPORTUNITY、PAMAP2、WISDM 和 Berkeley MHAD）上评估了三类模型：经典机器学习、深度学习架构以及受限玻尔兹曼机（RBMs）。比较了决策树、随机森林、卷积神经网络（CNN）和深度置信网络（DBNs）等模型，采用准确率、精确率、召回率和F1分数进行综合评估。结果表明，CNN在所有数据集上均表现优异，尤其在Berkeley MHAD上表现突出；经典模型如随机森林在小数据集上表现良好，但在大规模复杂数据上面临挑战；基于RBM的模型在特征学习方面也展现出显著潜力。本研究为研究人员选择合适的HAR模型提供了详细参考。
原文摘要 · Abstract (English)
Human Activity Recognition (HAR) has gained significant importance with the growing use of sensor-equipped devices and large datasets. This paper evaluates the performance of three categories of models : classical machine learning, deep learning architectures, and Restricted Boltzmann Machines (RBMs) using five key benchmark datasets of HAR (UCI-HAR, OPPORTUNITY, PAMAP2, WISDM, and Berkeley MHAD). We assess various models, including Decision Trees, Random Forests, Convolutional Neural Networks (CNN), and Deep Belief Networks (DBNs), using metrics such as accuracy, precision, recall, and F1-score for a comprehensive comparison. The results show that CNN models offer superior performance across all datasets, especially on the Berkeley MHAD. Classical models like Random Forest do well on smaller datasets but face challenges with larger, more complex data. RBM-based models also show notable potential, particularly for feature learning. This paper offers a detailed comparison to help researchers choose the most suitable model for HAR tasks.
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