Abstract A robust human activity recognition system using smartphone sensors and deep learning
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A robust human activity recognition system using smartphone sensors and deep learning

机译：使用智能手机传感器和深度学习的强大的人类活动识别系统

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AbstractIn last few decades, human activity recognition grabbed considerable research attentions from a wide range of pattern recognition and human–computer interaction researchers due to its prominent applications such as smart home health care. For instance, activity recognition systems can be adopted in a smart home health care system to improve their rehabilitation processes of patients. There are various ways of using different sensors for human activity recognition in a smartly controlled environment. Among which, physical human activity recognition through wearable sensors provides valuable information about an individual’s degree of functional ability and lifestyle. In this paper, we present a smartphone inertial sensors-based approach for human activity recognition. Efficient features are first extracted from raw data. The features include mean, median, autoregressive coefficients, etc. The features are further processed by a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) to make them more robust. Finally, the features are trained with a Deep Belief Network (DBN) for successful activity recognition. The proposed approach was compared with traditional expression recognition approaches such as typical multiclass Support Vector Machine (SVM) and Artificial Neural Network (ANN) where it outperformed them.Highlights•A smartphone inertial sensors-based approach for human activity recognition.•Uses deep learning based solution for successful activity recognition.•The proposed approach was compared with traditional expression recognition approaches.
机译： 摘要 在过去的几十年中，人类活动识别由于其出色的应用（例如智能家居健康）而吸引了众多模式识别和人机交互研究人员的研究关注。关心。例如，活动识别系统可以用于智能家庭保健系统中，以改善患者的康复过程。在智能控制的环境中，可以使用多种方法将不同的传感器用于人类活动的识别。其中，通过可穿戴式传感器进行的人体活动识别可以提供有关个人功能能力和生活方式的有价值的信息。在本文中，我们提出了一种基于智能手机惯性传感器的人类活动识别方法。首先从原始数据中提取有效特征。这些特征包括均值，中位数，自回归系数等。这些特征将通过内核主成分分析（KPCA）和线性判别分析（LDA）进行进一步处理，以使其更加稳健。最后，使用深层信任网络（DBN）对功能进行训练，以成功识别活动。将该方法与传统的表情识别方法进行了比较，例如传统的多类支持向量机（SVM）和人工神经网络（ANN），它们的表现均优于它们。 突出显示 •< / ce：label> 基于智能手机惯性传感器的人类活动识别方法。 • 使用基于深度学习的解决方案来成功识别活动。 • 道具将osed方法与传统的表情识别方法进行了比较。

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