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首页> 外文期刊>Procedia Computer Science >Feature extraction and feature selection in smartphone-based activity recognition
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Feature extraction and feature selection in smartphone-based activity recognition

机译：基于智能手机的活动识别功能提取与特征选择

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Nowadays, smartphones are gradually being integrated in our daily lives, and they can be considered powerful tools for monitoring human activities. However, due to the limitations of processing capability and energy consumption of smartphones compared to standard machines, a trade-off between performance and computational complexity must be considered when developing smartphone-based systems. In this paper, we shed light on the importance of feature selection and its impact on simplifying the activity classification process which enhances the computational complexity of the system. Through an in-depth survey on the features that are widely used in state-of-the-art studies, we selected the most common features for sensor-based activity classification, namely conventional features. Then, in an experimental study with 10 participants and using 2 different smartphones, we investigated how to reduce system complexity while maintaining classification performance by replacing the conventional feature set with an optimal set. For this reason, in the considered scenario, the users were instructed to perform different static and dynamic activities, while freely holding a smartphone in their hands. In our comparison to the state-of-the-art approaches, we implemented and evaluated major classification algorithms, including the decision tree and Bayesian network. We demonstrated that replacing the conventional feature set with an optimal set can significantly reduce the complexity of the activity recognition system with only a negligible impact on the overall system performance.
机译：如今，智能手机逐渐融入我们的日常生活中，它们可以被认为是监测人类活动的强大工具。然而，由于与标准机器相比，智能手机的处理能力和能耗的限制，在开发基于智能手机的系统时，必须考虑性能和计算复杂性之间的权衡。在本文中，我们阐明了特征选择的重要性及其对简化活动分类过程的影响，这提高了系统的计算复杂性。通过对最先进的研究中广泛用于广泛应用的功能的深入调查，我们为传统的活动分类选择了最常见的特征，即传统功能。然后，在具有10名参与者和使用2种不同智能手机的实验研究中，我们调查了如何通过用最佳集合替换传统功能集来减少系统复杂性，同时保持分类性能。出于这个原因，在考虑的场景中，用户被指示执行不同的静态和动态活动，同时在手中自由举行智能手机。在我们与最先进的方法的比较中，我们实施和评估了主要分类算法，包括决策树和贝叶斯网络。我们证明，用最佳集合替换传统功能集可以显着降低活动识别系统的复杂性，只对整个系统性能的影响忽略不计。

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