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Semi-supervised Adaptive Method for Human Activities Recognition (HAR)

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Computer Information Systems and Industrial Management (CISIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13293))

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Abstract

Using sensors and mobile devices integrated with hardware and software tools for Human Recognition Activities (HAR), is a growing scientific field, the analysis based on this information have promising benefits to detect regular and irregular behaviors in individuals during their daily activities. In this study, the Van Kasteren dataset was used for the experimental stage, and it all data was processed using the data mining classification methods: Decision Trees (DT), Support Vector Machines (SVM) and Naïve Bayes (NB). These methods were applied during the training and validation processes with the proposed methodology, and the results obtained showed that all these three methods were successful to identify the cluster associated to the activities contained in the Van Kasteren dataset. The Support Vector Machines (SVM) method showed the best results with the evaluation metrics: True Positive Rate (TPR) 99.2%, False Positive Rate (FPR) 0.6%, precision (99.2%), coverage (99.2%) and F-Measure (98.8%).

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Acknowledgment

This work was partially supported from the REMIND Project from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 734355.

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Authors and Affiliations

  1. Department of Computer Science and Electronics, Universidad de la Costa, Barranquilla, Colombia

    Fabio Mendoza Palechor, Alexis De la Hoz Manotas & Diego Molina Estren

  2. Department of Systems Engineering, Università degli Studi di Firenze, Firenze, Italy

    Enrico Vicario & Fulvio Patara

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  1. Fabio Mendoza Palechor
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  2. Enrico Vicario
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Correspondence to Fabio Mendoza Palechor , Alexis De la Hoz Manotas or Diego Molina Estren .

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  1. Bialystok University of Technology, Bialystok, Poland

    Khalid Saeed

  2. VSB - Technical University of Ostrava, Ostrava, Czech Republic

    Jiří Dvorský

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Palechor, F.M., Vicario, E., Patara, F., De la Hoz Manotas, A., Estren, D.M. (2022). Semi-supervised Adaptive Method for Human Activities Recognition (HAR). In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_1

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