Personalized Activity Recognition Using Partially Available Target Data
Personalized Activity Recognition Using Partially Available Target Data
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DOI：
10.1109/tmc.2021.3071434
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发表时间：
2023-01-01
影响因子：
7.9
通讯作者：
Ghasemzadeh, Hassan
中科院分区：
文献类型：
--
作者：
Fallahzadeh, Ramin;Ashari, Zhila Esna;Ghasemzadeh, Hassan
近年来，人们对自主活动识别模型的研究越来越多，这些模型用于在新环境中部署移动系统，例如当新用户采用可穿戴系统时。然而，目前的研究缺乏全面的迁移学习框架。具体来说，它缺乏在新设置中处理部分可用数据的能力。为了解决这些限制，我们提出了OptiMapper，这是一个用于活动识别的新型无信息跨主题迁移学习框架。OptiMapper是一个组合优化框架，它可以提取跨学科的抽象知识，并利用这些知识在新学科中开发个性化和准确的活动识别模型。为此，提出了一种新的基于社区检测的未标记数据聚类方法，该方法使用目标用户数据构建一个未注释的传感器观测网络。然后使用完全二部图模型将这些目标观测的簇映射到源簇上。在下一步中，映射的标签有条件地与基础学习器的预测融合，为目标用户创建个性化和标记的训练数据集。我们给出了OptiMapper的两个实例。第一个实例适用于具有相同活动标签的跨域迁移学习，在源用户和目标用户的集群之间执行一对一的二部映射。第二个实例化在源集群和目标集群之间执行最佳的多对一映射。多对一映射允许我们找到最佳映射，即使目标数据集不包含所有活动类的足够实例。我们证明了这种类型的跨域映射可以被表述为一个运输问题并得到最优解。我们在几个活动识别数据集上评估了我们的迁移学习技术。我们的结果表明，所提出的社区检测方法平均可以实现69%的数据集利用率，总体聚类精度为87.5%。我们的研究结果还表明，与最先进的技术相比，所提出的迁移学习算法可以实现高达22.5%的活动识别精度提高。实验结果表明，即使在部分数据存在的情况下，该方法也具有较高的持续性能。
Recent years have witnessed a growing body of research on autonomous activity recognition models for use in deployment of mobile systems in new settings such as when a wearable system is adopted by a new user. Current research, however, lacks comprehensive frameworks for transfer learning. Specifically, it lacks the ability to deal with partially available data in new settings. To address these limitations, we propose OptiMapper, a novel uninformed cross-subject transfer learning framework for activity recognition. OptiMapper is a combinatorial optimization framework that extracts abstract knowledge across subjects and utilizes this knowledge for developing a personalized and accurate activity recognition model in new subjects. To this end, a novel community-detection-based clustering of unlabeled data is proposed that uses the target user data to construct a network of unannotated sensor observations. The clusters of these target observations are then mapped onto the source clusters using a complete bipartite graph model. In the next step, the mapped labels are conditionally fused with the prediction of a base learner to create a personalized and labeled training dataset for the target user. We present two instantiations of OptiMapper. The first instantiation, which is applicable for transfer learning across domains with identical activity labels, performs a one-to-one bipartite mapping between clusters of the source and target users. The second instantiation performs optimal many-to-one mapping between the source clusters and those of the target. The many-to-one mapping allows us to find an optimal mapping even when the target dataset does not contain sufficient instances of all activity classes. We show that this type of cross-domain mapping can be formulated as a transportation problem and solved optimally. We evaluate our transfer learning techniques on several activity recognition datasets. Our results show that the proposed community detection approach can achieve, on average, 69 percent utilization of the datasets for clustering with an overall clustering accuracy of 87.5 percent. Our results also suggest that the proposed transfer learning algorithms can achieve up to 22.5 percent improvement in the activity recognition accuracy, compared to the state-of-the-art techniques. The experimental results also demonstrate high and sustained performance even in presence of partial data.