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Meta-heuristic based feature selection for aberration detection in human activity using smartphone inertial sensors

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Abstract

Wearable devices are equipped with inertial sensors that can collect motion data and provide objective measures of a person's physical activity. Smartphones are increasingly used to monitor users' activity, enabling accurate qualitative and quantitative measurement. In human activity patterns, aberration refers to any action that deviates from the normal or expected course of action. In the case of physical activity, aberrant activity must be continuously monitored and reported promptly in real-time scenarios. This study employs monitoring real-time physical activity features through the utilization of smartphone inertial sensors, to distinguish between aberrant and non-aberrant activity classes. Data from multiple sensors including accelerometer, gyroscope, magnetometer, and others, are collected from five participants' smartphones and synchronized to monitor activity. To obtain optimal set of statistical features, three meta-heuristic approaches—namely elephant search, wolf search, and cuckoo search—are utilized, and in combination with a correlation attribute, serve as feature filtering methods for feature selection. A new optimal feature set is derived from this process, resulting in a reduction in cardinality by approximately 43% and subsequently reducing the computational power required for analysis. The new feature set is employed to train supervised learning algorithms, which includes four baseline machine learning algorithms. Time to train model has been significantly reduced from 0.34 to 0.16, which is approximately 53% reduction. The results are compared before and after the feature selection process, and a comparative analysis is conducted. Random Forest algorithm proves to be the most effective, achieving an accuracy of 96.57% in predicting activity class.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India

    Sakshi, M. P. S. Bhatia & Pinaki Chakraborty

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  1. Sakshi
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  2. M. P. S. Bhatia
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  3. Pinaki Chakraborty
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Correspondence to Sakshi.

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Sakshi, Bhatia, M.P.S. & Chakraborty, P. Meta-heuristic based feature selection for aberration detection in human activity using smartphone inertial sensors. Int. j. inf. tecnol. 16, 559–568 (2024). https://doi.org/10.1007/s41870-023-01484-4

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