Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques

The research field on technologies and wireless sensor networks (WSN) are becoming one of the most disruptive technologies that support different scenarios of ubiquitous and generalized computing. WSN applied to the human body is generally called wireless body sensor networks. WSN can provide large...

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Autores:
Rocha, Jimmy Alfonso
Piñeres Espitia, Gabriel Dario
aziz, shariq
De-La-Hoz-Franco, Emiro
Tariq, Muhammad Imran
Carmine Sinito, Diego
Comas Gonzalez, Zhoe
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/9129
Acceso en línea:
https://hdl.handle.net/11323/9129
https://doi.org/10.1007/978-981-16-5036-9_31
https://repositorio.cuc.edu.co/
Palabra clave:
Wireless body sensor networks (WBSN)
C4.5 algorithm
LMT algorithm
SEMMA
Data mining
Rights
openAccess
License
Atribución 4.0 Internacional (CC BY 4.0)
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dc.title.eng.fl_str_mv Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
title Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
spellingShingle Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
Wireless body sensor networks (WBSN)
C4.5 algorithm
LMT algorithm
SEMMA
Data mining
title_short Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
title_full Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
title_fullStr Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
title_full_unstemmed Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
title_sort Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniques
dc.creator.fl_str_mv Rocha, Jimmy Alfonso
Piñeres Espitia, Gabriel Dario
aziz, shariq
De-La-Hoz-Franco, Emiro
Tariq, Muhammad Imran
Carmine Sinito, Diego
Comas Gonzalez, Zhoe
dc.contributor.author.spa.fl_str_mv Rocha, Jimmy Alfonso
Piñeres Espitia, Gabriel Dario
aziz, shariq
De-La-Hoz-Franco, Emiro
Tariq, Muhammad Imran
Carmine Sinito, Diego
Comas Gonzalez, Zhoe
dc.subject.proposal.eng.fl_str_mv Wireless body sensor networks (WBSN)
C4.5 algorithm
LMT algorithm
SEMMA
Data mining
topic Wireless body sensor networks (WBSN)
C4.5 algorithm
LMT algorithm
SEMMA
Data mining
description The research field on technologies and wireless sensor networks (WSN) are becoming one of the most disruptive technologies that support different scenarios of ubiquitous and generalized computing. WSN applied to the human body is generally called wireless body sensor networks. WSN can provide large quantities of data. The use of data mining techniques has allowed expanding WSN in new areas like biomedicine or telemedicine. The identification of psychological patterns and human activity recognition are two important trends to follow. In the current study, it is applied a SEMMA methodology to implement data mining clustering and classification techniques over RSS signal samples of a WBSN, based on IEEE 802.15.4 networks, with the intention of recognizing human activities based on samples. Two algorithms are applied, C4.5 and LTM for evaluate the rate success in the prediction.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-11-26
dc.date.accessioned.none.fl_str_mv 2022-04-18T14:32:18Z
dc.date.available.none.fl_str_mv 2022-04-18T14:32:18Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.citation.spa.fl_str_mv Rocha, J.A. et al. (2022). Human Activity Recognition Through Wireless Body Sensor Networks (WBSN) Applying Data Mining Techniques. In: Pan, JS., Balas, V.E., Chen, CM. (eds) Advances in Intelligent Data Analysis and Applications. Smart Innovation, Systems and Technologies, vol 253. Springer, Singapore. https://doi.org/10.1007/978-981-16-5036-9_31
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/9129
dc.identifier.url.spa.fl_str_mv https://doi.org/10.1007/978-981-16-5036-9_31
dc.identifier.doi.spa.fl_str_mv 10.1007/978-981-16-5036-9_31
dc.identifier.eissn.spa.fl_str_mv 2190-3026
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/
identifier_str_mv Rocha, J.A. et al. (2022). Human Activity Recognition Through Wireless Body Sensor Networks (WBSN) Applying Data Mining Techniques. In: Pan, JS., Balas, V.E., Chen, CM. (eds) Advances in Intelligent Data Analysis and Applications. Smart Innovation, Systems and Technologies, vol 253. Springer, Singapore. https://doi.org/10.1007/978-981-16-5036-9_31
2190-3018
10.1007/978-981-16-5036-9_31
2190-3026
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/9129
https://doi.org/10.1007/978-981-16-5036-9_31
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartofjournal.spa.fl_str_mv Smart Innovation, Systems and Technologies
dc.relation.references.spa.fl_str_mv Sohraby, K., Minoli, D., Znati, T.: Wireless Sensor Networks: Technology, Protocols, and Applications. Wiley, USA (2007)
Cama-Pinto, A., Piñeres-Espitia, G., Comas-González, Z., Vélez-Zapata, J., Gómez-Mula, F.: Diseño de una red de monitorización de variables meteorológicas relacionadas a los tornados en Barranquilla-Colombia y su área metropolitana. Ingeniare. Revista chilena de ingeniería 25(4), 585–598 (2017)
Yang, G.: Body Sensor Networks, vol. 1. Springer, London (2006)
Bouza, C.N., Santiago, A.: La minería de datos: árboles de decisión y su aplicación en estudios médicos. Modelación Matemática de Fenómenos del Medio Ambiente y la Salud 2, 64–78 (2012)
Pradeep, S., Kallimani, J.S.: A survey on various challenges and aspects in handling big data. In: 2017 Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 1–5. IEEE (2017)
Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Korting, T.S.: C4. 5 algorithm and multivariate decision trees. Image Processing Division, National Institute for Space Research–INPE Sao Jose dos Campos–SP, Brazil. (2006)
Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H.: Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik. Springer. Croatia (2003)
James, D.A.: The application of inertial sensors in elite sports monitoring. In: The Engineering of Sport, vol. 6, pp. 289–294. Springer, New York, NY (2006)
Merentitis, A., Kranitis, N., Paschalis, A., Gizopoulos, D.: Low energy online self-test of embedded processors in dependable WSN nodes. IEEE Trans. Dependable Secure Comput. 9(1), 86–100 (2011)
Llosa, J., Vilajosana, I., Vilajosana, X., Marquès, J.M.: Design of a motion detector to monitor rowing performance based on wireless sensor networks. In: 2009 International Conference on Intelligent Networking and Collaborative Systems, pp. 397–400. IEEE, Barcelona (2009)
Wu, J.K., Dong, L., Xiao, W.: Real-time physical activity classification and tracking using wearble sensors. In: 2007 6th International Conference on Information, Communications & Signal Processing, pp. 1–6. IEEE (2007)
Palumbo, F., Gallicchio, C., Pucci, R., Micheli, A.: Human activity recognition using multisensor data fusion based on reservoir computing. J. Ambient Intell. Smart Environ. 8(2), 87–107 (2016)
Palumbo, F., Barsocchi, P., Gallicchio, C., Chessa, S., Micheli, A.: Multisensor data fusion for activity recognition based on reservoir computing. In: International Competition on Evaluating AAL Systems through Competitive Benchmarking, pp. 24–35. Springer, Berlin, Heidelberg (2013)
Butt, S.A., Jamal, T., Azad, M.A., Ali, A., Safa, N.S.: A multivariant secure framework for smart mobile health application. Trans. Emerg. Telecommun. Technol. e3684 (2019)
Tambe, S.B., Thool, R.C., Thool, V.R.: Cluster based wireless mobile healthcare system for physiological data monitoring. Proc. Comput. Sci. 78, 40–47 (2016)
Synnes, K., Lilja, M., Nyman, A., Espinilla, M., Cleland, I., Comas, A.G.S., Comas-Gonzalez, Z., Hallberg, J., Karvonen, N., Ourique de Morais, W., Cruciani, F., Nugent, C.: H2Al—The human health and activity laboratory. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2(19), pp. 1241. Dominican Republic (2018)
Lee, J.H., Jeong, S.N., Choi, S.H.: Predictive data mining for diagnosing periodontal disease: the Korea National Health and Nutrition Examination Surveys (KNHANES V and VI) from 2010 to 2015. J. Public Health Dent. 79(1), 44–52 (2019)
Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., Salas-Navarro, K.: Parkinson disease analysis using supervised and unsupervised techniques. In: International Conference on Swarm Intelligence, July 2019, pp. 191–199. Springer, Cham (2019)
Singh, N., Kanthwal, A., Bidhuri, P.: Soccer competitiveness using shots on target: data mining approach. In: International Conference on Human-Computer Interaction, pp. 141–150. Springer, Cham (2019)
Jamal, T., Butt, S.A.: Malicious node analysis in MANETS. Int. J. Inf. Technol. 1–9 (2018)
De-La-Hoz-Correa, E., Mendoza Palechor, F., De-La-Hoz-Manotas, A., Morales Ortega, R., Sánchez Hernández, A.B.: Obesity level estimation software based on decision trees. J. Comput. Sci. 15(1), 67–77 (2019)
De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition—a systematic review of literature. IEEE Access 6, 59192–59210 (2018)
Butt, S.A., Diaz-Martinez, J.L., Jamal, T., Ali, A., De-La-Hoz-Franco, E., Shoaib, M.: IoT smart health security threats. In: 2019 19th International Conference on Computational Science and Its Applications (ICCSA), Saint Petersburg, Russia, 2019, pp. 26–31. https://doi.org/10.1109/ICCSA.2019.000-8
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dc.rights.spa.fl_str_mv Atribución 4.0 Internacional (CC BY 4.0)
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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spelling Rocha, Jimmy AlfonsoPiñeres Espitia, Gabriel Darioaziz, shariqDe-La-Hoz-Franco, EmiroTariq, Muhammad ImranCarmine Sinito, DiegoComas Gonzalez, Zhoe2022-04-18T14:32:18Z2022-04-18T14:32:18Z2021-11-26Rocha, J.A. et al. (2022). Human Activity Recognition Through Wireless Body Sensor Networks (WBSN) Applying Data Mining Techniques. In: Pan, JS., Balas, V.E., Chen, CM. (eds) Advances in Intelligent Data Analysis and Applications. Smart Innovation, Systems and Technologies, vol 253. Springer, Singapore. https://doi.org/10.1007/978-981-16-5036-9_312190-3018https://hdl.handle.net/11323/9129https://doi.org/10.1007/978-981-16-5036-9_3110.1007/978-981-16-5036-9_312190-3026Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The research field on technologies and wireless sensor networks (WSN) are becoming one of the most disruptive technologies that support different scenarios of ubiquitous and generalized computing. WSN applied to the human body is generally called wireless body sensor networks. WSN can provide large quantities of data. The use of data mining techniques has allowed expanding WSN in new areas like biomedicine or telemedicine. The identification of psychological patterns and human activity recognition are two important trends to follow. In the current study, it is applied a SEMMA methodology to implement data mining clustering and classification techniques over RSS signal samples of a WBSN, based on IEEE 802.15.4 networks, with the intention of recognizing human activities based on samples. Two algorithms are applied, C4.5 and LTM for evaluate the rate success in the prediction.1 páginaapplication/pdfengSpringer VerlagGermanyAtribución 4.0 Internacional (CC BY 4.0)© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Human activity recognition through wireless body sensor networks (WBSN) applying data mining techniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://link.springer.com/chapter/10.1007/978-981-16-5036-9_31Smart Innovation, Systems and TechnologiesSohraby, K., Minoli, D., Znati, T.: Wireless Sensor Networks: Technology, Protocols, and Applications. Wiley, USA (2007)Cama-Pinto, A., Piñeres-Espitia, G., Comas-González, Z., Vélez-Zapata, J., Gómez-Mula, F.: Diseño de una red de monitorización de variables meteorológicas relacionadas a los tornados en Barranquilla-Colombia y su área metropolitana. Ingeniare. Revista chilena de ingeniería 25(4), 585–598 (2017)Yang, G.: Body Sensor Networks, vol. 1. Springer, London (2006)Bouza, C.N., Santiago, A.: La minería de datos: árboles de decisión y su aplicación en estudios médicos. Modelación Matemática de Fenómenos del Medio Ambiente y la Salud 2, 64–78 (2012)Pradeep, S., Kallimani, J.S.: A survey on various challenges and aspects in handling big data. In: 2017 Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp. 1–5. IEEE (2017)Jain, A.K.: Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)Korting, T.S.: C4. 5 algorithm and multivariate decision trees. Image Processing Division, National Institute for Space Research–INPE Sao Jose dos Campos–SP, Brazil. (2006)Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H.: Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik. Springer. Croatia (2003)James, D.A.: The application of inertial sensors in elite sports monitoring. In: The Engineering of Sport, vol. 6, pp. 289–294. Springer, New York, NY (2006)Merentitis, A., Kranitis, N., Paschalis, A., Gizopoulos, D.: Low energy online self-test of embedded processors in dependable WSN nodes. IEEE Trans. Dependable Secure Comput. 9(1), 86–100 (2011)Llosa, J., Vilajosana, I., Vilajosana, X., Marquès, J.M.: Design of a motion detector to monitor rowing performance based on wireless sensor networks. In: 2009 International Conference on Intelligent Networking and Collaborative Systems, pp. 397–400. IEEE, Barcelona (2009)Wu, J.K., Dong, L., Xiao, W.: Real-time physical activity classification and tracking using wearble sensors. In: 2007 6th International Conference on Information, Communications & Signal Processing, pp. 1–6. IEEE (2007)Palumbo, F., Gallicchio, C., Pucci, R., Micheli, A.: Human activity recognition using multisensor data fusion based on reservoir computing. J. Ambient Intell. Smart Environ. 8(2), 87–107 (2016)Palumbo, F., Barsocchi, P., Gallicchio, C., Chessa, S., Micheli, A.: Multisensor data fusion for activity recognition based on reservoir computing. In: International Competition on Evaluating AAL Systems through Competitive Benchmarking, pp. 24–35. Springer, Berlin, Heidelberg (2013)Butt, S.A., Jamal, T., Azad, M.A., Ali, A., Safa, N.S.: A multivariant secure framework for smart mobile health application. Trans. Emerg. Telecommun. Technol. e3684 (2019)Tambe, S.B., Thool, R.C., Thool, V.R.: Cluster based wireless mobile healthcare system for physiological data monitoring. Proc. Comput. Sci. 78, 40–47 (2016)Synnes, K., Lilja, M., Nyman, A., Espinilla, M., Cleland, I., Comas, A.G.S., Comas-Gonzalez, Z., Hallberg, J., Karvonen, N., Ourique de Morais, W., Cruciani, F., Nugent, C.: H2Al—The human health and activity laboratory. In: Multidisciplinary Digital Publishing Institute Proceedings, vol. 2(19), pp. 1241. Dominican Republic (2018)Lee, J.H., Jeong, S.N., Choi, S.H.: Predictive data mining for diagnosing periodontal disease: the Korea National Health and Nutrition Examination Surveys (KNHANES V and VI) from 2010 to 2015. J. Public Health Dent. 79(1), 44–52 (2019)Ariza-Colpas, P., Morales-Ortega, R., Piñeres-Melo, M., De la Hoz-Franco, E., Echeverri-Ocampo, I., Salas-Navarro, K.: Parkinson disease analysis using supervised and unsupervised techniques. In: International Conference on Swarm Intelligence, July 2019, pp. 191–199. Springer, Cham (2019)Singh, N., Kanthwal, A., Bidhuri, P.: Soccer competitiveness using shots on target: data mining approach. In: International Conference on Human-Computer Interaction, pp. 141–150. Springer, Cham (2019)Jamal, T., Butt, S.A.: Malicious node analysis in MANETS. Int. J. Inf. Technol. 1–9 (2018)De-La-Hoz-Correa, E., Mendoza Palechor, F., De-La-Hoz-Manotas, A., Morales Ortega, R., Sánchez Hernández, A.B.: Obesity level estimation software based on decision trees. J. Comput. Sci. 15(1), 67–77 (2019)De-La-Hoz-Franco, E., Ariza-Colpas, P., Quero, J.M., Espinilla, M.: Sensor-based datasets for human activity recognition—a systematic review of literature. IEEE Access 6, 59192–59210 (2018)Butt, S.A., Diaz-Martinez, J.L., Jamal, T., Ali, A., De-La-Hoz-Franco, E., Shoaib, M.: IoT smart health security threats. In: 2019 19th International Conference on Computational Science and Its Applications (ICCSA), Saint Petersburg, Russia, 2019, pp. 26–31. https://doi.org/10.1109/ICCSA.2019.000-8339327Wireless body sensor networks (WBSN)C4.5 algorithmLMT algorithmSEMMAData miningPublicationORIGINALHuman activity recognition through wireless body sensor networks (WBSN) applying data mining techniques.pdfHuman activity recognition through wireless body sensor networks (WBSN) applying data mining techniques.pdfapplication/pdf57826https://repositorio.cuc.edu.co/bitstreams/2754312a-a05b-473e-868c-6a00d114318f/download21a219091f95d80125400450d6f42360MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/8e48fb0c-ef08-4dda-a1dd-51888ec1e85a/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTHuman activity recognition through wireless body sensor networks (WBSN) applying data mining techniques.pdf.txtHuman activity recognition through wireless body sensor networks 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