Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification.
Pruning Growing Self-Organizing Map Network for Human Physical Activity Identification.
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DOI：
10.1155/2022/9972406
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发表时间：
2022
影响因子：
--
通讯作者：
Hua W
中科院分区：
文献类型：
--
作者：
Mo L;Yu H;Hua W
基于可穿戴传感器的人体身体活动识别对于人体健康分析具有重要意义。大量的机器学习模型被应用于人体体力活动识别，并取得了显著的效果。然而，大多数人体身体活动识别模型只能基于标记数据进行训练，难以获得足够的标记数据，导致模型泛化能力较弱。针对小规模标注数据集在训练阶段无监督的局限性，提出了一种剪枝生长SOM模型，然后只使用少量标注数据对神经元进行标注，以减少对标注数据的依赖。在训练阶段，通过剪枝机制删除网络中不活跃的神经元，使模型更符合数据分布，即使在不平衡数据集上也能提高识别精度，尤其是对识别效果较差的动作类别。此外，剪枝机制还可以通过控制模型的规模来加快模型的推理速度。
Human physical activity identification based on wearable sensors is of great significance to human health analysis. A large number of machine learning models have been applied to human physical activity identification and achieved remarkable results. However, most human physical activity identification models can only be trained based on labeled data, and it is difficult to obtain enough labeled data, which leads to weak generalization ability of the model. A Pruning Growing SOM model is proposed in this paper to address the limitations of small-scale labeled dataset, which is unsupervised in the training stage, and then only a small amount of labeled data is used for labeling neurons to reduce dependency on labeled data. In training stage, the inactive neurons in network can be deleted by pruning mechanism, which makes the model more consistent with the data distribution and improves the identification accuracy even on unbalanced dataset, especially for the action categories with poor identification effect. In addition, the pruning mechanism can also speed up the inference of the model by controlling its scale.
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