Few-shot learning-based human activity recognition
Few-shot learning-based human activity recognition
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DOI：
10.1016/j.eswa.2019.06.070
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发表时间：
2019-12-30
影响因子：
8.5
通讯作者：
Duarte, Marco F.
中科院分区：
文献类型：
--
作者：
Feng, Siwei;Duarte, Marco F.
少样本学习是一种通过从相关任务转移知识来学习具有极少量标记训练数据的模型的技术。本文提出了一种基于可穿戴传感器的人体活动识别的小样本学习方法，这是一种从低级传感器输入中寻求高级人类活动知识的技术。由于获取人类生成的活动数据的成本很高，而且活动模式之间普遍存在相似性，当只有少量数据可用于模型训练时，从现有活动识别模型中借用信息比收集更多数据从头开始训练新模型更有效。所提出的少样本人类活动识别方法利用深度学习模型进行特征提取和分类，同时以模型参数传递的方式进行知识传递。为了减轻负迁移，提出了一种度量来衡量跨领域的类相关性，以便在知识迁移过程中为相关性较高的知识分配较大的权重。大量实验的有希望的结果表明了所提出方法的优点。 (C) 2019 Elsevier Ltd. 保留所有权利。
Few-shot learning is a technique to learn a model with a very small amount of labeled training data by transferring knowledge from relevant tasks. In this paper, a few-shot learning method for wearable sensor based human activity recognition, which is a technique that seeks high-level human activity knowledge from low-level sensor inputs, is proposed. Due to the high costs to obtain human generated activity data and the ubiquitous similarities between activity modes, it can be more efficient to borrow information from existing activity recognition models than to collect more data to train a new model from scratch when only a few data are available for model training. The proposed few-shot human activity recognition method leverages a deep learning model for feature extraction and classification while knowledge transfer is performed in the manner of model parameter transfer. In order to alleviate negative transfer, a metric is proposed to measure cross-domain class-wise relevance so that knowledge of higher relevance is assigned larger weights during knowledge transfer. Promising results in extensive experiments show the advantages of the proposed approach. (C) 2019 Elsevier Ltd. All rights reserved.