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Arm movement activity based user authentication in P2P systems
Peer-to-Peer Networking and Applications ( IF 4.2 ) Pub Date : 2019-06-21 , DOI: 10.1007/s12083-019-00775-7
Jungpil Shin , Md Rashedul Islam , Md Abdur Rahim , Hyung-Jin Mun

User authentication has become an essential security element that enables a wide range of applications in P2P systems for higher security and safety requirements. In previous, many researchers worked on user authentication based on certificates, passwords, and feature-based authentication (e.g. face recognition, fingerprint detection, iris recognition, voice recognition). However, authentication using those technologies may fail because this information can be easily shared among users or synthesized. Also, there are several cyber and cryptography attacks. With the progress of the latest sensor technology, wearable as Microsoft Bands, Fitbit, and Garmin has provided for more information collecting opportunities. From those above point of views, this paper presents a novel user identification system based on the bio signal analysis of arm movement (3-axis accelerometer & 3-axis gyroscope) and electromyography (EMG) signal using Myo armband as a wearable user authentication system in P2P system that identifies users based on the bio-signal of movement of a person’s arm. In this study, the gesture and EMG signals are obtained from the sensor and denoised using wavelet denoising algorithm. The denoised signals are analyzed using the envelope and cepstrum analysis for extracting the potential feature vector. Finally, the feature vector is used to train and identify a user using multi-class support vector machine (MC-SVM) with different kernel function for user authentication. For validating the proposed authentication model, signals are obtained from the arm movements, i.e., directions and hand gesture data using acceleration, gyroscope and EMG sensors of several subjects. According to the experimental results, the proposed model shows satisfactory performance. To evaluate the efficiency of the proposed systems, we measure and compare its classification accuracy with state-of-the-art algorithms. And the proposed algorithm outperforms with others.

中文翻译:

P2P系统中基于手臂运动活动的用户身份验证

用户身份验证已成为必不可少的安全元素,可以使P2P系统中的各种应用程序具有更高的安全性和安全性要求。以前,许多研究人员致力于基于证书,密码和基于功能的身份验证(例如,面部识别,指纹检测,虹膜识别,语音识别)进行用户身份验证。但是,使用这些技术进行身份验证可能会失败,因为此信息可以在用户之间轻松共享或合成。另外,还有几种网络和加密攻击。随着最新传感器技术的进步,可穿戴式Microsoft Bands,Fitbit和Garmin提供了更多的信息收集机会。从以上观点来看,本文提出了一种新颖的用户识别系统,该系统基于对手臂运动(3轴加速度计和3轴陀螺仪)和肌电图(EMG)信号进行生物信号分析,并使用Myo臂带作为P2P系统中的可穿戴用户身份验证系统,该系统可识别用户在一个人手臂运动的生物信号上。在这项研究中,手势和EMG信号从传感器获得,并使用小波去噪算法进行去噪。使用包络和倒频谱分析来分析去噪的信号,以提取潜在的特征向量。最后,特征向量用于使用具有不同内核功能的多类支持向量机(MC-SVM)来训练和识别用户,以进行用户身份验证。为了验证所提出的认证模型,需要从手臂运动中获取信号,即 使用多个对象的加速度,陀螺仪和EMG传感器来确定方向和手势数据。根据实验结果,提出的模型表现出令人满意的性能。为了评估所提出系统的效率,我们使用最新算法测量并比较了其分类精度。所提算法优于其他算法。
更新日期:2019-06-21
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