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 Journal of Advanced Computational Intelligence and Intelligent Informatics Pub Date : 2023-09-20 DOI:10.20965/jaciii.2023.p0915
Huailiang Xia, Xiaoyan Zhao, Yan Chen, Tianyao Zhang, Yuguo Yin, Zhaohui Zhang
{"title":"基于稀疏惯性传感器的人体运动捕捉与识别","authors":"Huailiang Xia, Xiaoyan Zhao, Yan Chen, Tianyao Zhang, Yuguo Yin, Zhaohui Zhang","doi":"10.20965/jaciii.2023.p0915","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":45921,"journal":{"name":"Journal of Advanced Computational Intelligence and Intelligent Informatics","volume":"126 1","pages":"0"},"PeriodicalIF":0.7000,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Human Motion Capture and Recognition Based on Sparse Inertial Sensor\",\"authors\":\"Huailiang Xia, Xiaoyan Zhao, Yan Chen, Tianyao Zhang, Yuguo Yin, Zhaohui Zhang\",\"doi\":\"10.20965/jaciii.2023.p0915\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":45921,\"journal\":{\"name\":\"Journal of Advanced Computational Intelligence and Intelligent Informatics\",\"volume\":\"126 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Advanced Computational Intelligence and Intelligent Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20965/jaciii.2023.p0915\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Advanced Computational Intelligence and Intelligent Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jaciii.2023.p0915","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
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。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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.
期刊最新文献
The Impact of Individual Heterogeneity on Household Asset Choice: An Empirical Study Based on China Family Panel Studies Private Placement, Investor Sentiment, and Stock Price Anomaly Does Increasing Public Service Expenditure Slow the Long-Term Economic Growth Rate?—Evidence from China Prediction and Characteristic Analysis of Enterprise Digital Transformation Integrating XGBoost and SHAP Industrial Chain Map and Linkage Network Characteristics of Digital Economy
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1