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Physiological Signal Based Entity Authentication for Body Area Sensor Networks and Mobile Healthcare Systems

机译:人体感应器网络和移动医疗系统基于生理信号的实体认证

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With the evolution of m-Health, an increasing number of biomedical sensors will be worn on or implanted in an individual in the future for the monitoring, diagnosis, and treatment of diseases. For the optimization of resources, it is therefore necessary to investigate how to interconnect these sensors in a wireless body area network, wherein security of private data transmission is always a major concern. This paper proposes a novel solution to tackle the problem of entity authentication in body area sensor network (BASN) for m-Health. Physiological signals detected by biomedical sensors have dual functions: (1) for a specific medical application, and (2) for sensors in the same BASN to recognize each other by biometrics. A feasibility study of proposed entity authentication scheme was carried out on 12 healthy individuals, each with 2 channels of photoplethysmogram (PPG) captured simultaneously at different parts of the body. The beat-to-beat heartbeat interval is used as a biometric characteristic to generate identity of the individual. The results of statistical analysis suggest that it is a possible biometric feature for the entity authentication of BASN
机译:随着m-Health的发展,将来越来越多的生物医学传感器将被佩戴或植入个人中,以监测,诊断和治疗疾病。为了优化资源,因此有必要研究如何在无线人体局域网中互连这些传感器,其中私有数据传输的安全性始终是主要问题。本文提出了一种新颖的解决方案,用于解决人体健康传感器在人体区域传感器网络(BASN)中的实体认证问题。生物医学传感器检测到的生理信号具有双重功能:(1)用于特定医学应用;(2)同一BASN中的传感器通过生物识别技术相互识别。拟议的实体认证方案的可行性研究是针对12位健康的个体进行的,每个个体在身体的不同部位同时捕获2个光体积描记图(PPG)通道。逐次心跳间隔被用作生物特征以生成个体的身份。统计分析结果表明,这是BASN实体身份验证的一种可能的生物特征。

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