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Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machine

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Abstract

The growing interest in utilizing smartphone detectors to distinguish between transportation modes stems from their applications in health monitoring, urban transport planning, and location-based services. This study introduces a model utilizing data from smartphone accelerometers, magnetometers, and gyroscopes to identify various transportation modes. The novel Imbalanced Maximizing-Area Under the Curve Proximal Support Vector Machine (ImAUC-PSVM) technique refines the conventional PSVM for automated transportation mode identification. The ImAUC-PSVM method brings several benefits: 1) It embeds AUC maximization within its objective function, streamlining the model by reducing the need for extensive parameter adjustments, thus making it highly suitable for training with imbalanced databases; 2) Theoretical examination confirms that ImAUC-PSVM preserves the fundamental structure of the standard PSVM, including its benefits, especially in situations that require rapid and ongoing updates for effective transportation mode classification. The model is enhanced by a specialized Differential Evolution (DE) framework, which excels in exploring complex hyperparameter landscapes. The efficacy of the model is confirmed utilizing an imbalanced database from HTC, including contributions from numerous participants, totaling over eight thousand hours and several dozen gigabytes of data. Based on established evaluation criteria, our findings indicate that the model achieves notable precision, proving to be a powerful tool for transportation mode classification. Code is publicly available at https://github.com/Zhenhua-Dai/Mode-identification-ImAUC-PSVM/.

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Acknowledgements

I would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.

Funding

This work was supported by the Key Scientific Research Foundation of Hunan Provincial Department of Education (No. 23A0575); in part by the Hunan Provincial Natural Science Foundation (Nos. 2024JJ7184, 2024JJ7187); in part by the the Project of Hunan Provincial Social Science Achievement Review Committee in 2023 (No.XSP2023JYC283); in part by the Science Communication Research and Practice Project of Hunan Association for Science and Technology in 2023 (No.2023jckpkt096) and in part by the General Research Projects of Hunan Provincial Department of Education(No.23C0358).

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  1. School of Information Engineering, Hunan University of Science and Engineering, Yongzhou, 425199, Hunan, China

    Zhenhua Dai & Tangsen Huang

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All authors contributed to the study’s conception and design. Data collection. simulation and analysis were performed by " Zhenhua Dai. and Tangsen Huang ". The first draft of the manuscript was written by " Zhenhua Dai ".

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Dai, Z., Huang, T. Smartphone detector examination for transportation mode identification utilizing imbalanced maximizing-area under the curve proximal support vector machine. SIViP 18, 8361–8377 (2024). https://doi.org/10.1007/s11760-024-03479-5

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  • DOI: https://doi.org/10.1007/s11760-024-03479-5

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