[1]吴秋雯,廖薇.基于可穿戴设备的姿势识别方法研究[J].中国医学物理学杂志,2023,40(8):1002-1008.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.013]
　WU Qiuwen,LIAO Wei.Pose recognition method based on wearable device[J].Chinese Journal of Medical Physics,2023,40(8):1002-1008.[doi:DOI:10.3969/j.issn.1005-202X.2023.08.013]
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基于可穿戴设备的姿势识别方法研究()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
40卷
期数:
2023年第8期
页码:
1002-1008
栏目:
医学信号处理与医学仪器
出版日期:
2023-09-01

文章信息/Info

Title:
Pose recognition method based on wearable device
文章编号:
1005-202X（2023）08-1002-07
作者:
吴秋雯; 廖薇
上海工程技术大学电子电气工程学院， 上海 201620
Author(s):
WU Qiuwen;  LIAO Wei
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
关键词:
可穿戴设备; 人体区域通信; 信道衰减; 姿势识别
Keywords:
Keywords: wearable device human body area communication channel attenuation pose recognition
分类号:
R318；TN915
DOI:
DOI:10.3969/j.issn.1005-202X.2023.08.013
文献标志码:
A
摘要:
针对现有的基于图像以及毫米波雷达进行人体姿势识别在医疗保健领域存在的不足，提出一种基于人体区域通信技术的穿戴式姿势识别方法。该方法通过人体姿势导致的信道衰减进行姿势识别。文中首先建立了人体站、蹲、坐、跑、走5种姿势模型，并在人体上放置1个发射器、2个接收器，构成两条信道，计算5种姿势下的信道衰减作为数据集。然后，对两条信道分别使用多种单一分类器进行识别，获得94.33%的最高准确率。最后，通过Stacking方法融合两条信道的特征得到99.67%的准确率。结果表明，基于人体区域通信技术的穿戴式姿势识别方法是可行的，并利用Stacking特征融合方法使识别结果相比单一特征下的最高准确率提高了5.34%。
Abstract:
Abstract: A pose recognition method using wearable devices and human body area communication technology is proposed to overcome the drawbacks of current methods based on image and millimeter wave radar and realize the recognition through the channel attenuation caused by human posture. Five postures of human standing, squatting, sitting, running and walking are modeled, and two channels are constructed with one transmitter and two receivers for calculating the channel attenuation with 5 postures as the data set. The highest recognition accuracy of multiple single classifiers for the two channels is 94.33%, and the accuracy is improved to 99.67% with feature fusion of the two channels using Stacking method. The results show that the pose recognition method using wearable devices and human area communication technology is feasible, and that the highest accuracy rate using Stacking feature fusion is improved by 5.34% as compared with single feature.

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备注/Memo

备注/Memo:
【收稿日期】2023-03-26 【基金项目】国家自然科学基金（62001282） 【作者简介】吴秋雯，硕士研究生，研究方向：人体区域通信，E-mail:wuqiuwen97@163.com 【通信作者】廖薇，副教授，上海高校青年东方学者，硕士生导师，研究方向：人体区域通信、生物电磁学、医疗领域电磁兼容性，E-mail: liaowei54@126.com
更新日期/Last Update: 2023-09-06