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Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications (CIARP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14469))

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Abstract

The ubiquity of consumer devices with sensing and computational capabilities, such as smartphones and smartwatches, has increased interest in their use in human activity recognition for healthcare monitoring applications, among others. When developing such a system, researchers rely on input data to train recognition models. In the absence of openly available datasets that meet the model requirements, researchers face a hard and time-consuming process to decide which sensing device to use or how much data needs to be collected. In this paper, we explore the effect of the amount of training data on the performance (i.e., classification accuracy and activity-wise F1-scores) of a CNN model by performing an incremental cross-subject evaluation using data collected from a consumer smartphone and smartwatch. Systematically studying the incremental inclusion of subject data from a set of 22 training subjects, the results show that the model’s performance initially improves significantly with each addition, yet this improvement slows down the larger the number of included subjects. We compare the performance of models based on smartphone and smartwatch data. The latter option is significantly better with smaller sizes of training data, while the former outperforms with larger amounts of training data. In addition, gait-related activities show significantly better results with smartphone-collected data, while non-gait-related activities, such as standing up or sitting down, were better recognized with smartwatch-collected data.

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Notes

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    https://www.st.com/en/mems-and-sensors/lsm6dso.html.

  2. 2.

    https://time.google.com.

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Acknowledgments

M. Matey-Sanz and A. González-Pérez are funded by the Spanish Ministry of Universities [grants FPU19/05352 and FPU17/03832]. This study was supported by project PID2020-120250RB-I00 (SyMptOMS-ET) funded by MCIN/AEI/10.13039/501100011033.

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Authors and Affiliations

  1. Institute of New Imaging Technologies, Universitat Jaume I, 12071, Castellón, Spain

    Miguel Matey-Sanz, Alberto González-Pérez, Sven Casteleyn & Carlos Granell

  2. ALGORITMI Research Centre, University of Minho, 4800-058, Guimarães, Portugal

    Joaquín Torres-Sospedra

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  1. Miguel Matey-Sanz
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Correspondence to Miguel Matey-Sanz .

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Editors and Affiliations

  1. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Verónica Vasconcelos

  2. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Inês Domingues

  3. Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

    Simão Paredes

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Matey-Sanz, M., Torres-Sospedra, J., González-Pérez, A., Casteleyn, S., Granell, C. (2024). Analysis and Impact of Training Set Size in Cross-Subject Human Activity Recognition. In: Vasconcelos, V., Domingues, I., Paredes, S. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2023. Lecture Notes in Computer Science, vol 14469. Springer, Cham. https://doi.org/10.1007/978-3-031-49018-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-49018-7_28

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