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中国医疗器械杂志 2020, Vol. 44 Issue (4) :288-293    DOI: 10.3969/j.issn.1671-7104.2020.04.002
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无线健康监护系统设计及状态识别算法
杨雷,王志武,姜萍萍,颜国正,刘大生,韩玎,赵凯
上海交通大学 电子信息与电气工程学院,上海市,200240
Design of Wearable Wireless Health Monitoring System and Status Recognition Algorithm
YANG Lei, WANG Zhiwu, JIANG Pingping, YAN Guozheng, LIU Dasheng, HAN Ding, ZHAO Kai
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University,Shanghai, 200240

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摘要 该研究提出了一种面向强制戒毒所戒毒人员的穿戴式无线健康监护系统。该系统可以连续实时地监测戒毒 人员的多项生理参数,当生理参数出现异常发出预警信息,起到及时行医的作用。此外,提出了一种卷积 神经网络模型(convolutional neural network, CNN),该模型根据多项生理参数对戒毒人员的健康状态 进行评估。实验表明,将该模型用于公开的生理参数数据集的身体状态识别任务,在单个实验对象的生理 参数数据集上最高可达100%的识别准确率;当使用整个生理参数数据集时可获得高达99.1%的识别准确 率,超过了传统的模式识别方法在此任务上的表现,验证了该模型的优越性。
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关键词戒毒人员   穿戴式   ZigBee   健康监护   卷积神经网络   分类识别     
Abstract: A wearable wireless health monitoring system for drug addicts in compulsory rehabilitation centers was proposed. The system can continuously monitor multiple physiological parameters of drug addicts in real time, and issue early warning information when abnormal physiological parameters occur, so as to play the role of timely medical practice. In addition, this study proposes a convolutional neural network (CNN)model, which can evaluate the health status of drug addicts based on multiple physiological parameters. Experiments show that the model can be applied to the task of body state recognition in the open physiological parameter data set, and the recognition accuracy can reach up to 100% in a single physiological parameter data set; when the whole physiological data set is used, the recognition accuracy can reach 99.1%. The recognition accuracy exceeds the performance of the traditional pattern recognition method on this task, which verifies the superiority of the model.
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Received 2019-11-06;
Fund:

国家重点研发计划项目(2018YFC0807405)

Corresponding Authors: 颜国正     Email: gzhyan@sjtu.edu.cn
引用本文:   
杨雷,王志武,姜萍萍,颜国正,刘大生,韩玎,赵凯.无线健康监护系统设计及状态识别算法[J]  中国医疗器械杂志, 2020,V44(4): 288-293
YANG Lei, WANG Zhiwu, JIANG Pingping, YAN Guozheng, LIU Dasheng, HAN Ding, ZHAO Kai.Design of Wearable Wireless Health Monitoring System and Status Recognition Algorithm[J]  Chinese Journal of Medical Instrumentation, 2020,V44(4): 288-293
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