Semi-supervised adaptive method for human activities recognition (HAR)

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. I...

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Autores:
Mendoza Palechor, Fabio
Vicario, Enrico
PATARA, FULVIO
De la Hoz Manotas, Alexis Kevin
Molina Estren, Diego
Tipo de recurso:
Part of book
Fecha de publicación:
2022
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9522
Acceso en línea:
https://hdl.handle.net/11323/9522
https://doi.org/10.1007/978-3-031-10539-5_1
https://repositorio.cuc.edu.co/
Palabra clave:
HAR
Data mining
Cluster
Evaluation metric
Dataset
Van Karesten
Rights
embargoedAccess
License
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
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dc.title.eng.fl_str_mv Semi-supervised adaptive method for human activities recognition (HAR)
title Semi-supervised adaptive method for human activities recognition (HAR)
spellingShingle Semi-supervised adaptive method for human activities recognition (HAR)
HAR
Data mining
Cluster
Evaluation metric
Dataset
Van Karesten
title_short Semi-supervised adaptive method for human activities recognition (HAR)
title_full Semi-supervised adaptive method for human activities recognition (HAR)
title_fullStr Semi-supervised adaptive method for human activities recognition (HAR)
title_full_unstemmed Semi-supervised adaptive method for human activities recognition (HAR)
title_sort Semi-supervised adaptive method for human activities recognition (HAR)
dc.creator.fl_str_mv Mendoza Palechor, Fabio
Vicario, Enrico
PATARA, FULVIO
De la Hoz Manotas, Alexis Kevin
Molina Estren, Diego
dc.contributor.author.spa.fl_str_mv Mendoza Palechor, Fabio
Vicario, Enrico
PATARA, FULVIO
De la Hoz Manotas, Alexis Kevin
Molina Estren, Diego
dc.subject.proposal.eng.fl_str_mv HAR
Data mining
Cluster
Evaluation metric
Dataset
Van Karesten
topic HAR
Data mining
Cluster
Evaluation metric
Dataset
Van Karesten
description 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%).
publishDate 2022
dc.date.accessioned.none.fl_str_mv 2022-09-19T13:57:25Z
dc.date.available.none.fl_str_mv 2022-09-19T13:57:25Z
dc.date.issued.none.fl_str_mv 2022
dc.type.spa.fl_str_mv Capítulo - Parte de Libro
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_3248
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/bookPart
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dc.identifier.citation.spa.fl_str_mv 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
dc.identifier.isbn.spa.fl_str_mv 978-3-031-10538-8
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9522
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1007/978-3-031-10539-5_1
dc.identifier.doi.spa.fl_str_mv 10.1007/978-3-031-10539-5_1
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
dc.identifier.eisbn.spa.fl_str_mv 978-3-031-10539-5
identifier_str_mv 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
978-3-031-10538-8
10.1007/978-3-031-10539-5_1
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
978-3-031-10539-5
url https://hdl.handle.net/11323/9522
https://doi.org/10.1007/978-3-031-10539-5_1
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofseries.spa.fl_str_mv Computer Information Systems and Industrial Management. CISIM 2022.;
dc.relation.ispartofbook.spa.fl_str_mv Lecture Notes in Computer Science
dc.relation.references.spa.fl_str_mv Ladjailia, A., Bouchrika, I., Merouani, H.F., Harrati, N., Mahfouf, Z.: Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput. Appl. 32(21), 16387–16400 (2019). https://doi.org/10.1007/s00521-018-3951-x
Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily living activity recognition based on statistical feature quality group selection. Expert Syst. Appl. 39(9), 8013–8021 (2012)
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Arifoglu, D., Bouchachia, A.: Activity recognition and abnormal behaviour detection with recurrent neural networks. Proc. Comput. Sci. 110, 86–93 (2017)
Fergani, B.: Comparing HMM, LDA, SVM and Smote-SVM algorithms in classifying human activities. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds.) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol. 381, pp. 639–644. Springer, Cham (2016)
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spelling Mendoza Palechor, FabioVicario, EnricoPATARA, FULVIODe la Hoz Manotas, Alexis KevinMolina Estren, Diego2022-09-19T13:57:25Z2022-09-19T13:57:25Z2022Palechor, 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_1978-3-031-10538-8https://hdl.handle.net/11323/9522https://doi.org/10.1007/978-3-031-10539-5_110.1007/978-3-031-10539-5_1Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-031-10539-5Using 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%).1 páginaapplication/pdfengSpringer, ChamSwitzerlandComputer Information Systems and Industrial Management. CISIM 2022.;Lecture Notes in Computer ScienceLadjailia, A., Bouchrika, I., Merouani, H.F., Harrati, N., Mahfouf, Z.: Human activity recognition via optical flow: decomposing activities into basic actions. Neural Comput. Appl. 32(21), 16387–16400 (2019). https://doi.org/10.1007/s00521-018-3951-xBanos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily living activity recognition based on statistical feature quality group selection. Expert Syst. Appl. 39(9), 8013–8021 (2012)Casale, P., Pujol, O., Radeva, P.: Human activity recognition from accelerometer data using a wearable device. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 289–296. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21257-4_36Calabria-Sarmiento, J.C., et al.: (2018). Software applications to health sector: a systematic review of literature (2018)Chen, L., Hoey, J., Nugent, C.D., Cook, D.J., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 790–808 (2012)Arifoglu, D., Bouchachia, A.: Activity recognition and abnormal behaviour detection with recurrent neural networks. Proc. Comput. Sci. 110, 86–93 (2017)Fergani, B.: Comparing HMM, LDA, SVM and Smote-SVM algorithms in classifying human activities. In: El Oualkadi, A., Choubani, F., El Moussati, A. (eds.) Proceedings of the Mediterranean Conference on Information & Communication Technologies 2015. Lecture Notes in Electrical Engineering, vol. 381, pp. 639–644. Springer, Cham (2016)Paul, P., George, T.: An effective approach for human activity recognition on smartphone. In: 2015 IEEE International Conference on Engineering and Technology (Icetech), pp. 1–3. IEEE, March 2015Dao, M.S., Nguyen-Gia, T.A., Mai, V.C.: Daily human activities recognition using heterogeneous sensors from smartphones. Proc. Comput. Sci. 111, 323–328 (2017)Liu, Y., Nie, L., Liu, L., Rosenblum, D.S.: From action to activity: sensor-based activity recognition. Neurocomputing 181, 108–115 (2016) Concone, F., Gaglio, S., Lo Re, G., Morana, M.: Smartphone data analysis for human activity recognition. 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J. 1(6), 90–95 (2013)© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfSemi-supervised adaptive method for human activities recognition (HAR)Capítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://link.springer.com/chapter/10.1007/978-3-031-10539-5_1HARData miningClusterEvaluation metricDatasetVan KarestenPublicationORIGINALSemi-supervised adaptive method for human activities recognition (HAR).pdfSemi-supervised adaptive method for human activities recognition 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