Smartphone-based Human Activity Recognition (HAR) uses spatiotemporal time series data collected from a smartphone’s in-built accelerometer, gyroscope, and magnetometer sensor. Real-time HAR datasets such as UCI-HAR and UCI-HAPT extract time and frequency domain statistical features for activity recognition from the collected time series signals. Various Machine Learning techniques have been proposed in the perspective of feature selection through ensemble, traditional, hybrid, and meta-heuristic-based techniques to improve the recognition performance. Though the feature selection techniques have shown promising results, the computational time and selection of valid significant features for better classification performance still need to be included. Moreover, these techniques are highly parameter-dependent and critical to threshold values used for experimentation. This research proposes a novel method of parameter-free ensemble feature selection by combining traditional feature selection algorithms with the concept of association rule mining to obtain the Maximal Feature Subset (MFS), which is used for training and testing the classification model for HAR. Mathematical association rule mining is framed rather than the traditional one to select the associated frequent feature(s) from the feature set evolved by traditional techniques. Experimentation has been carried out on UCI-HAR and UCI-HAPT, and the proposed method has increased the classification efficiency by selecting significant features and reduces computation time significantly.

中文翻译：

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基于智能手机的人类活动识别的集成最大特征子集选择

基于智能手机的人类活动识别 (HAR) 使用从智能手机内置的加速度计、陀螺仪和磁力计传感器收集的时空时间序列数据。实时 HAR 数据集（例如 UCI-HAR 和 UCI-HAPT）从收集的时间序列信号中提取时域和频域统计特征，用于活动识别。人们从特征选择的角度提出了各种机器学习技术，通过集成、传统、混合和基于元启发式的技术来提高识别性能。尽管特征选择技术已经显示出有希望的结果，但仍然需要包括计算时间和有效重要特征的选择，以获得更好的分类性能。此外，这些技术高度依赖于参数，并且对用于实验的阈值至关重要。本研究提出了一种新的无参数集成特征选择方法，将传统特征选择算法与关联规则挖掘的概念相结合，获得最大特征子集（MFS），用于训练和测试 HAR 分类模型。数学关联规则挖掘不是传统的，而是从传统技术发展而来的特征集中选择关联的频繁特征。在UCI-HAR和UCI-HAPT上进行了实验，该方法通过选择重要特征提高了分类效率，并显着减少了计算时间。