基于稀疏惯性传感器的人体运动捕捉与识别

IF 0.7 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Huailiang Xia, Xiaoyan Zhao, Yan Chen, Tianyao Zhang, Yuguo Yin, Zhaohui Zhang
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引用次数: 0

摘要

人体动作捕捉技术领域代表了一个新兴的、多方面的领域,它包含了各种学科,包括但不限于计算机图形学、人体工程学和通信技术。在其领域内建立了独特的网络平台,保证了数据传输的可靠性和稳定性。此外,还配置了一个汇聚节点,以便通过两个不同的通道接收传感器数据。值得注意的是,测量系统的简单性与使用的传感器数量有限成正比。本研究的重点是通过可穿戴惯性传感器的稀疏排列准确估计不确定的人体3D运动,仅利用系统内的六个传感器。该方法基于整个运动过程的时间序列序列,其中一系列不连续的动作构成了顺序运动。采用深度学习方法,特别是递归神经网络来优化回归参数。我们的方法集成了历史和现在的传感器数据来预测未来的传感器数据。这些数据被合并成一个叠加的输入向量,然后反馈到一个浅神经网络中来估计人体运动。我们的实验结果证明了这种方法的可行性:六个传感器可以准确地复制具有代表性的姿势。这一发现对于在动作捕捉领域推进和应用可穿戴设备具有重要意义,为广泛采用和实施提供了潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Human Motion Capture and Recognition Based on Sparse Inertial Sensor
The field of human motion capture technology represents an emergent and multifaceted domain that encapsulates various disciplines, including but not limited to computer graphics, ergonomics, and communication technology. A distinct network platform within its domain has been established to ensure the reliability and stability of data transmission. Moreover, a sink node has been configured to facilitate sensor data reception through two distinct channels. Notably, the simplicity of the measurement system is directly proportional to the limited number of sensors used. This study focuses on accurately estimating uncertain human 3D movements via a sparse arrangement of wearable inertial sensors, utilizing only six sensors within the system. The methodology is based on a time series sequence throughout the motion process, wherein a series of discontinuous actions constitute the sequential motion. Deep learning methodologies, specifically recurrent neural networks, were employed to refine the regression parameters. Our approach integrated both historical and present sensor data to forecast future sensor data. These data were amalgamated into a superposed input vector, which was fed back into a shallow neural network to estimate human motion. Our experimental results demonstrate the viability of this approach: the six sensors could accurately replicate representative poses. This finding carries significant implications for advancing and applying wearable devices within the realm of motion capture, offering the potential for widespread adoption and implementation.
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来源期刊
CiteScore
1.50
自引率
14.30%
发文量
89
期刊介绍: JACIII focuses on advanced computational intelligence and intelligent informatics. The topics include, but are not limited to; Fuzzy logic, Fuzzy control, Neural Networks, GA and Evolutionary Computation, Hybrid Systems, Adaptation and Learning Systems, Distributed Intelligent Systems, Network systems, Multi-media, Human interface, Biologically inspired evolutionary systems, Artificial life, Chaos, Complex systems, Fractals, Robotics, Medical applications, Pattern recognition, Virtual reality, Wavelet analysis, Scientific applications, Industrial applications, and Artistic applications.
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