A holistic multi-source transfer learning approach using wearable sensors for personalized daily activity recognition

IF 5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Complex & Intelligent Systems Pub Date : 2023-09-13 DOI:10.1007/s40747-023-01218-w
Qi Jia, Jing Guo, Po Yang, Yun Yang
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Abstract

Abstract Human activity recognition (HAR) aims to collect time series through wearable devices to precisely identify specific actions. However, the traditional HAR method ignores the activity variances among individuals, which will cause low generalization when applied to a new individual and indirectly enhance the difficulties of personalized HAR service. In this paper, we fully consider activity divergence among individuals to develop an end-to-end model, the multi-source unsupervised co-transfer network (MUCT), to provide personalized activity recognition for new individuals. We denote the collected data of different individuals as multiple domains and implement deep domain adaptation to align each pair of source and target domains. In addition, we propose a consistent filter that utilizes two heterogeneous classifiers to automatically select high-confidence instances from the target domain to jointly enhance the performance on the target task. The effectiveness and performance of our model are evaluated through comprehensive experiments on two activity recognition benchmarks and a private activity recognition data set (collected by our signal sensors), where our model outperforms traditional transfer learning methods at HAR.

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使用可穿戴传感器进行个性化日常活动识别的整体多源迁移学习方法
人体活动识别(HAR)旨在通过可穿戴设备收集时间序列,以精确识别特定的动作。然而,传统的HAR方法忽略了个体之间的活动差异,在应用于新个体时泛化程度较低,间接增加了个性化HAR服务的难度。在本文中,我们充分考虑个体之间的活动差异,建立了一个端到端模型,即多源无监督共同转移网络(MUCT),为新个体提供个性化的活动识别。我们将收集到的不同个体的数据表示为多个域,并实现深度域自适应以对齐每对源域和目标域。此外,我们提出了一个一致性过滤器,利用两个异构分类器从目标域中自动选择高置信度的实例,以共同提高目标任务上的性能。我们的模型的有效性和性能是通过在两个活动识别基准和一个私人活动识别数据集(由我们的信号传感器收集)上的综合实验来评估的,我们的模型优于HAR的传统迁移学习方法。
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来源期刊
Complex & Intelligent Systems
Complex & Intelligent Systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
9.60
自引率
10.30%
发文量
297
期刊介绍: Complex & Intelligent Systems aims to provide a forum for presenting and discussing novel approaches, tools and techniques meant for attaining a cross-fertilization between the broad fields of complex systems, computational simulation, and intelligent analytics and visualization. The transdisciplinary research that the journal focuses on will expand the boundaries of our understanding by investigating the principles and processes that underlie many of the most profound problems facing society today.
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