{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:05:08Z","timestamp":1771517108891,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T00:00:00Z","timestamp":1467676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.<\/jats:p>","DOI":"10.3390\/s16071033","type":"journal-article","created":{"date-parts":[[2016,7,5]],"date-time":"2016-07-05T10:06:19Z","timestamp":1467713179000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6559-7501","authenticated-orcid":false,"given":"Hiram","family":"Ponce","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Universidad Panamericana, Mexico City 03920, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9038-7821","authenticated-orcid":false,"given":"Mar\u00eda","family":"Mart\u00ednez-Villase\u00f1or","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad Panamericana, Mexico City 03920, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luis","family":"Miralles-Pechu\u00e1n","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, Universidad Panamericana, Mexico City 03920, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,7,5]]},"reference":[{"key":"ref_1","unstructured":"Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., and Havinga, P. (2010, January 22\u201325). Activity recognition using inertial sensing for healthcare, wellbeing and sports applications: A survey. Proceedings of the 23rd International Conference on Architecture of Computing Systems, Hannover, Germany."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/0967-3334\/30\/4\/R01","article-title":"Activity identification using body-mounted sensors-a review of classification techniques","volume":"30","author":"Preece","year":"2009","journal-title":"Physiol. Meas."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.jbi.2014.07.009","article-title":"A Smartphone-driven methodology for estimating physical activities and energy expenditure in free livng conditions","volume":"52","author":"Guidoux","year":"2014","journal-title":"J. Biomed. Inform."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-based activity recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. Syst. Man. Cybern. C Appl. Rev."},{"key":"ref_5","unstructured":"Ugulino, W., Cardador, D., Vega, K., Velloso, E., Milidi\u00fa, R., and Fuks, H. (2012). Advances in Artificial Intelligence\u2014SBIA 2012, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Reyes, J. (2015). Smartphone-Based Human Activity Recognition, Springer. Springer Theses.","DOI":"10.1007\/978-3-319-14274-6"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/j.jbi.2010.01.004","article-title":"Discovery of high-level tasks in the operating room","volume":"44","author":"Bouarfa","year":"2011","journal-title":"J. Biomed. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10462-010-9156-z","article-title":"A study of the effect of different types of noise on the precision of supervised learning techniques","volume":"33","author":"Nettleton","year":"2010","journal-title":"Artif. Intell. Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on human activity recognition using wearable sensors","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., F\u00f6rster, K., Tr\u00f6ster, G., Lukowicz, P., Bannach, D., Pirkl, G., and Ferscha, A. (2010, January 15\u201318). Collecting complex activity datasets in highly rich networked sensor environments. Proceedings of the IEEE Seventh International Conference on Networked Sensing Systems, Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_11","first-page":"55","article-title":"Application and comparison of modified classifiers for human activity recognition","volume":"89","author":"Moravec","year":"2013","journal-title":"Prz. Elektrotech."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Reiss, A., and Stricker, D. (2012, January 18\u201322). Introducing a new benchmarked dataset for activity monitoring. Proceedings of the IEEE 16th International Symposium on Wearable Computers (ISWC), Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_13","unstructured":"Reiss, A., and Stricker, D. (, January August). PAMAP2 physical activity monitoring monitoring data set. Dataset from the Department Augmented Vision, DFKI, Saarbr\u00fccken, Germany."},{"key":"ref_14","unstructured":"Gordon, D., Schmidtke, H., and Beigl, M. (2010, January 17). Introducing new sensors for activity recognition. Proceedings of the Workshop on How to Do Good Research in Activity Recognition at the 8th International Conference on Pervasive Computing, Helsinki, Finland."},{"key":"ref_15","first-page":"1541","article-title":"Activity recognition from accelerometer data","volume":"Volume 3","author":"Ravi","year":"2005","journal-title":"Proceedings of the 17th Conference on Innovative Applications of Artificial Intelligence (IAAI)"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sagha, H., Digumarti, S.T., Mill\u00e1n, J.D.R., Chavarriaga, R., Calatroni, A., Roggen, D., and Tr\u00f6ster, G. (2011, January 9\u201312). Benchmarking classification techniques using the opportunity human activity dataset. Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Anchorage, AK, USA.","DOI":"10.1109\/ICSMC.2011.6083628"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.3390\/s150102059","article-title":"A survey of online activity recognition using mobile phones","volume":"15","author":"Shoaib","year":"2015","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1007\/s00371-012-0752-6","article-title":"A survey on activity recognition and behavior understanding in video surveillance","volume":"29","author":"Vishwakarma","year":"2013","journal-title":"Vis. Comput."},{"key":"ref_20","unstructured":"Chavarriaga, R., Roggen, D., and Ferscha, A. (2011). Workshop on Robust Machine Learning Techniques for Human Activity Recognition, IEEE."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ponce, H., Martinez-Villase\u00f1or, L., and Miralles-Pechuan, L. (2015). Comparative Analysis of Artificial Hydrocarbon Networks and Data-Driven Approaches for Human Activity Recognition, Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-319-26401-1_15"},{"key":"ref_22","unstructured":"Lara, O. (2012). On the Automatic Recognition of Human Activities Using Heterogeneous Wearable Sensors. [Ph.D. Thesis, University of South California]."},{"key":"ref_23","first-page":"205","article-title":"Fall detection and activity recognition with machine learning","volume":"33","year":"2009","journal-title":"Informatica"},{"key":"ref_24","unstructured":"Ross, R., and Kelleher, J. (2013). Evolving Ambient Intelligence, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1016\/j.patrec.2012.12.014","article-title":"The Opportunity challenge: A benchmark database for on-body sensor-based activity recognition","volume":"34","author":"Chavarriaga","year":"2013","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ponce, H., and Ayala-Solares, R. (2016). The Power of Natural Inspiration in Control Systems, Springer. Studies in Systems, Decision and Control.","DOI":"10.1007\/978-3-319-26230-7_1"},{"key":"ref_27","unstructured":"Ponce, H., Ponce, P., and Molina, A. (2014). Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks, Springer. Studies in Computational Intelligence."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1002\/jcc.23818","article-title":"The Development of an Artificial Organic Networks Toolkit for LabVIEW","volume":"36","author":"Ponce","year":"2015","journal-title":"J. Comput. Chem."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"8858","DOI":"10.1016\/j.eswa.2015.07.041","article-title":"A Novel Robust Liquid Level Controller for Coupled-Tanks Systems Using Artificial Hydrocarbon Networks","volume":"42","author":"Ponce","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2013\/531031","article-title":"Artificial Hydrocarbon Networks Fuzzy Inference System","volume":"2013","author":"Ponce","year":"2013","journal-title":"Math. Probl. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6512","DOI":"10.1016\/j.eswa.2013.12.040","article-title":"Adaptive Noise Filtering Based on Artificial Hydrocarbon Networks: An Application to Audio Signals","volume":"41","author":"Ponce","year":"2014","journal-title":"Expert Syst. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A tutorial on human activity recognition using body-worn inertial sensors","volume":"46","author":"Bulling","year":"2014","journal-title":"ACM Comput. Surv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"31314","DOI":"10.3390\/s151229858","article-title":"Physical Human Activity Recognition Using Wearable Sensors","volume":"15","author":"Attal","year":"2015","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1023\/A:1012487302797","article-title":"Gene selection for cancer classification using support vector machines","volume":"46","author":"Guyon","year":"2002","journal-title":"Mach. Learn."},{"key":"ref_35","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","article-title":"Class Noise vs. Attribute Noise: A quantitative Study","volume":"22","author":"Zhu","year":"2004","journal-title":"Artif. Intell. Rev."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","article-title":"The WEKA Data Mining Software: An Update","volume":"11","author":"Hall","year":"2009","journal-title":"SIGKDD Explor."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.ipm.2009.03.002","article-title":"A systematic analysis of performance measures for classification tasks","volume":"45","author":"Sokolova","year":"2009","journal-title":"Inf. Process. Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/7\/1033\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:25:25Z","timestamp":1760210725000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/7\/1033"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,5]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2016,7]]}},"alternative-id":["s16071033"],"URL":"https:\/\/doi.org\/10.3390\/s16071033","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,5]]}}}