PDCHAR: Human activity recognition via multi-sensor wearable networks using two-channel convolutional neural networks

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yvxuan Ren, Dandan Zhu, Kai Tong, Lulu Xv, Zhengtai Wang, Lixin Kang, Jinguo Chai
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引用次数: 0

Abstract

Realizing human activity recognition is an important issue in pedestrian navigation and intelligent prosthetic control. Utilizing miniature multi-sensor wearable networks is a reliable method to improve the efficiency and convenience of the recognition system. Effective feature extraction and fusion of multimodal signals is a key issue in recognition. Therefore, this paper proposes an enhanced algorithm based on PCA sensor coupling analysis for data preprocessing. Subsequently, an innovative two-channel convolutional neural network with an SPF feature fusion layer as the core is built. The network fully analyzes the local and global features of multimodal signals using the local contrast and luminance properties of feature images. Compared with traditional methods, the model can reduce the data dimensionality and automatically identify and fuse the key information of the signals. In addition, most of the current mode recognition only supports simple actions such as walking and running, this paper constructs a database containing sixteen states by building a network with inertial sensors (IMU), curvature sensors (FLEX) and electromyography sensors (EMG). The experimental results show that the proposed system exhibits better results in complex action recognition and provides a new scheme for the realization of feature fusion and enhancement.

Abstract Image

Abstract Image

PDC-HAR:利用双通道卷积神经网络通过多传感器可穿戴网络识别人类活动
实现人类活动识别是行人导航和智能假肢控制中的一个重要问题。利用微型多传感器可穿戴网络是提高识别系统效率和便利性的可靠方法。对多模态信号进行有效的特征提取和融合是识别中的一个关键问题。因此,本文提出了一种基于 PCA 传感器耦合分析的增强算法,用于数据预处理。随后,构建了以 SPF 特征融合层为核心的创新型双通道卷积神经网络。该网络利用特征图像的局部对比度和亮度特性,全面分析多模态信号的局部和全局特征。与传统方法相比,该模型可以降低数据维度,自动识别和融合信号的关键信息。此外,目前的模式识别大多只支持行走和跑步等简单动作,本文通过建立一个包含惯性传感器(IMU)、曲率传感器(FLEX)和肌电传感器(EMG)的网络,构建了一个包含十六种状态的数据库。实验结果表明,所提出的系统在复杂动作识别方面表现出更好的效果,并为实现特征融合和增强提供了一种新方案。
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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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