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Daily Human Physical Activity Recognition Based on Kernel Discriminant Analysis and Extreme Learning Machine

机译：基于核判别分析和极限学习机的日常人类体育活动识别

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摘要

Wearable sensor based human physical activity recognition has extensive applications in many fields such as physical training and health care. This paper will be focused on the development of highly efficient approach for daily human activity recognition by a triaxial accelerometer. In the proposed approach, a number of features, including the tilt angle, the signal magnitude area (SMA), and the wavelet energy, are extracted from the raw measurement signal via the time domain, the frequency domain, and the time-frequency domain analysis. A nonlinear kernel discriminant analysis (KDA) scheme is introduced to enhance the discrimination between different activities. Extreme learning machine (ELM) is proposed as a novel activity recognition algorithm. Experimental results show that the proposed KDA based ELM classifier can achieve superior recognition performance with higher accuracy and faster learning speed than the back-propagation (BP) and the support vector machine (SVM) algorithms.
机译：基于可穿戴传感器的人体体育活动识别在体育锻炼和医疗保健等许多领域具有广泛的应用。本文将重点研究通过三轴加速度计识别日常人类活动的高效方法。在提出的方法中，通过时域，频域和时频域从原始测量信号中提取了许多特征，包括倾斜角，信号幅度区域（SMA）和小波能量。分析。引入非线性核判别分析（KDA）方案以增强不同活动之间的区别。极限学习机（ELM）被提出作为一种新颖的活动识别算法。实验结果表明，与反向传播算法和支持向量机算法相比，基于KDA的ELM分类器具有更高的识别性能，更高的准确度和更快的学习速度。

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  • 来源
    《Mathematical Problems in Engineering》 |2015年第9期|790412.1-790412.8|共8页
  • 作者

    Xiao Wendong; Lu Yingjie;

  • 作者单位

    Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.;

    Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China.;

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