{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:11:12Z","timestamp":1761808272702,"version":"build-2065373602"},"reference-count":110,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T00:00:00Z","timestamp":1556496000000},"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>Sensors are becoming more and more ubiquitous as their price and availability continue to improve, and as they are the source of information for many important tasks. However, the use of sensors has to deal with noise and failures. The lack of reliability in the sensors has led to many forms of redundancy, but simple solutions are not always the best, and the precise way in which several sensors are combined has a big impact on the overall result. In this paper, we discuss how to deal with the combination of information coming from different sensors, acting thus as \u201cvirtual sensors\u201d, in the context of human activity recognition, in a systematic way, aiming for optimality. To achieve this goal, we construct meta-datasets containing the \u201csignatures\u201d of individual datasets, and apply machine-learning methods in order to distinguish when each possible combination method could be actually the best. We present specific results based on experimentation, supporting our claims of optimality.<\/jats:p>","DOI":"10.3390\/s19092017","type":"journal-article","created":{"date-parts":[[2019,4,29]],"date-time":"2019-04-29T07:01:22Z","timestamp":1556521282000},"page":"2017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Virtual Sensors for Optimal Integration of Human Activity Data"],"prefix":"10.3390","volume":"19","author":[{"given":"Antonio A.","family":"Aguileta","sequence":"first","affiliation":[{"name":"Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico"},{"name":"Facultad de Matem\u00e1ticas, Universidad Aut\u00f3noma de Yucat\u00e1n, Anillo Perif\u00e9rico Norte, Tablaje Cat. 13615, Colonia Chuburn\u00e1 Hidalgo Inn, M\u00e9rida, Yucat\u00e1n 97110, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0995-2273","authenticated-orcid":false,"given":"Ramon F.","family":"Brena","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico"}]},{"given":"Oscar","family":"Mayora","sequence":"additional","affiliation":[{"name":"Fandazione Bruno Kessler Foundation, 38123 Trento, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7615-4431","authenticated-orcid":false,"given":"Erik","family":"Molino-Minero-Re","sequence":"additional","affiliation":[{"name":"Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas\u2014Sede M\u00e9rida, Unidad Acad\u00e9mica de Ciencias y Tecnolog\u00eda de la UNAM en Yucat\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Sierra Papacal, Yucat\u00e1n 97302, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9741-4581","authenticated-orcid":false,"given":"Luis A.","family":"Trejo","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, School of Engineering and Sciences, Carretera al Lago de Guadalupe Km. 3.5, Atizap\u00e1n de Zaragoza, Estado de M\u00e9xico 52926, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/2.237456","article-title":"Ubiquitous computing","volume":"26","author":"Weiser","year":"1993","journal-title":"Computer"},{"key":"ref_2","unstructured":"Hansmann, U., Merk, L., Nicklous, M.S., and Stober, T. (2003). Pervasive Computing: The Mobile World, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MPRV.2002.993141","article-title":"The computer for the 21st century","volume":"1","author":"Weiser","year":"2002","journal-title":"IEEE Pervasive Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.inffus.2016.09.005","article-title":"Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges","volume":"35","author":"Gravina","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1367-5788(02)80018-9","article-title":"Virtual sensors for control applications","volume":"26","author":"Albertos","year":"2002","journal-title":"Annu. Rev. Control"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kabadayi, S., Pridgen, A., and Julien, C. (2006). Virtual Sensors: Abstracting Data from Physical Sensors. Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks, IEEE Computer Society.","DOI":"10.1109\/WOWMOM.2006.115"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Gustafsson, F., Persson, N., Drev\u00f6, M., Forssell, U., Quicklund, H., and L\u00f6fgren, M. (2001). Virtual Sensors of Tire Pressure and Road Friction, Link\u00f6ping University Electronic Press.","DOI":"10.4271\/2001-01-0796"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1086\/505293","article-title":"Novel methods for predicting photometric redshifts from broadband photometry using virtual sensors","volume":"647","author":"Way","year":"2006","journal-title":"Astrophys. J."},{"key":"ref_9","unstructured":"Ciciriello, P., Mottola, L., and Picco, G.P. (December, January 27). Building virtual sensors and actuators over logical neighborhoods. Proceedings of the International Workshop on Middleware for Sensor Networks, Melbourne, Australia."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1007\/s007790170019","article-title":"Understanding and Using Context","volume":"5","author":"Dey","year":"2001","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Huynh, T., Fritz, M., and Schiele, B. (2008, January 21\u201324). Discovery of Activity Patterns Using Topic Models. Proceedings of the 10th International Conference on Ubiquitous Computing, Seoul, Korea.","DOI":"10.1145\/1409635.1409638"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gellersen, H.W. (1999). Towards a Better Understanding of Context and Context-Awareness. Handheld and Ubiquitous Computing, Springer.","DOI":"10.1007\/3-540-48157-5"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ponce, H., Miralles-Pechu\u00e1n, L., and Mart\u00ednez-Villase\u00f1or, M.d.L. (2016). A Flexible Approach for Human Activity Recognition Using Artificial Hydrocarbon Networks. Sensors, 16.","DOI":"10.3390\/s16111715"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"579","DOI":"10.1109\/JBHI.2012.2234129","article-title":"A Survey on Ambient-Assisted Living Tools for Older Adults","volume":"17","author":"Rashidi","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Frontoni, E., Raspa, P., Mancini, A., Zingaretti, P., and Placidi, V. (2013). Customers\u2019 activity recognition in intelligent retail environments. International Conference on Image Analysis and Processing, Springer.","DOI":"10.1007\/978-3-642-41190-8_55"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/1743-0003-2-6","article-title":"A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation","volume":"2","author":"Jovanov","year":"2005","journal-title":"J. NeuroEng. Rehabil."},{"key":"ref_18","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 6\u201313). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the 2011 IEEE international conference on Computer vision (ICCV), Barcelona, Spain."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/S0167-8655(00)00118-5","article-title":"Performance evaluation in content-based image retrieval: Overview and proposals","volume":"22","author":"Squire","year":"2001","journal-title":"Pattern Recognit. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"8961","DOI":"10.3390\/s140508961","article-title":"A ubiquitous and low-cost solution for movement monitoring and accident detection based on sensor fusion","volume":"14","author":"Felisberto","year":"2014","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, C.W., and Narayanan, S. (2016, January 21\u201323). Comparison of feature-level and kernel-level data fusion methods in multi-sensory fall detection. Proceedings of the 2016 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada.","DOI":"10.1109\/MMSP.2016.7813381"},{"key":"ref_22","unstructured":"Liggins, M.E., Hall, D.L., and Llinas, J. (2008). Handbook of Multisensor Data Fusion: Theory and Practice, CRC Press."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5687","DOI":"10.3390\/s140305687","article-title":"Multi-sensor fusion for enhanced contextual awareness of everyday activities with ubiquitous devices","volume":"14","author":"Guiry","year":"2014","journal-title":"Sensors"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Adelsberger, R., and Tr\u00f6ster, G. (2013, January 2\u20135). Pimu: A wireless pressure-sensing imu. Proceedings of the 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia.","DOI":"10.1109\/ISSNIP.2013.6529801"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1088\/0967-3334\/32\/9\/009","article-title":"Calibrating a novel multi-sensor physical activity measurement system","volume":"32","author":"John","year":"2011","journal-title":"Physiol. Meas."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11063-014-9395-0","article-title":"Multi-sensor fusion based on asymmetric decision weighting for robust activity recognition","volume":"42","author":"Banos","year":"2015","journal-title":"Neural Process. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xiao, L., Li, R., Luo, J., and Duan, M. (2013). Activity recognition via distributed random projection and joint sparse representation in body sensor networks. China Conference Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-642-54522-1_6"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.inffus.2017.06.004","article-title":"Multi-view stacking for activity recognition with sound and accelerometer data","volume":"40","author":"Brena","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1109\/3468.618255","article-title":"Application of majority voting to pattern recognition: An analysis of its behavior and performance","volume":"27","author":"Lam","year":"1997","journal-title":"IEEE Trans. Syst. Man, Cybern.-Part A Syst. Hum."},{"key":"ref_30","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 2010 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., and Havinga, P.J. (2016). Complex human activity recognition using smartphone and wrist-worn motion sensors. Sensors, 16.","DOI":"10.3390\/s16040426"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., and Cook, D.J. (2012, January 26\u201327). Simple and complex activity recognition through smart phones. Proceedings of the 2012 8th International Conference on Intelligent Environments (IE), Guanajuato, Mexico.","DOI":"10.1109\/IE.2012.39"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Brena, R.F., and Nava, A. (2016). Activity Recognition in Meetings with One and Two Kinect Sensors. Mexican Conference on Pattern Recognition, Springer.","DOI":"10.1007\/978-3-319-39393-3_22"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.1007\/s10044-016-0549-8","article-title":"Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone","volume":"19","author":"Lee","year":"2016","journal-title":"Pattern Anal. Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TMM.2017.2726187","article-title":"Deep Temporal Multimodal Fusion for Medical Procedure Monitoring Using Wearable Sensors","volume":"20","author":"Bernal","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1016\/j.amepre.2012.11.004","article-title":"Using the SenseCam to improve classifications of sedentary behavior in free-living settings","volume":"44","author":"Kerr","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.amepre.2012.11.007","article-title":"Using a wearable camera to increase the accuracy of dietary analysis","volume":"44","author":"Cullen","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.amepre.2012.11.005","article-title":"Benefits of SenseCam review on neuropsychological test performance","volume":"44","author":"Silva","year":"2013","journal-title":"Am. J. Prev. Med."},{"key":"ref_39","unstructured":"Tacconi, D., Mayora, O., Lukowicz, P., Arnrich, B., Tr\u00f6ster, G., and Haring, C. (2007, January 21\u201324). On the Feasibility of Using Activity Recognition and Context Aware Interaction to Support Early Diagnosis of Bipolar Disorder. Proceedings of the Ubicomp, Ubiwell Workshop, Seoul, Korea."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Rad, N.M., Kia, S.M., Zarbo, C., Jurman, G., Venuti, P., and Furlanello, C. (2016, January 12\u201315). Stereotypical motor movement detection in dynamic feature space. Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain.","DOI":"10.1109\/ICDMW.2016.0076"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Diraco, G., Leone, A., and Siciliano, P. (2016). A Fall Detector Based on Ultra-Wideband Radar Sensing. Convegno Nazionale Sensori, Springer.","DOI":"10.1007\/978-3-319-55077-0_47"},{"key":"ref_42","unstructured":"Alam, M.A.U. (2017, January 13\u201317). Context-aware multi-inhabitant functional and physiological health assessment in smart home environment. Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gjoreski, H., Lustrek, M., and Gams, M. (2011, January 25\u201328). Accelerometer placement for posture recognition and fall detection. Proceedings of the 2011 7th International Conference on Intelligent Environments (IE), Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Li, Q., and Stankovic, J.A. (2011, January 5\u20137). Grammar-based, posture-and context-cognitive detection for falls with different activity levels. Proceedings of the 2nd Conference on Wireless Health, Kos Island, Greece.","DOI":"10.1145\/2077546.2077553"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Wei, Y., Fei, Q., and He, L. (2014, January 27\u201328). Sports motion analysis based on mobile sensing technology. Proceedings of the International Conference on Global Economy, Finance and Humanities Research (GEFHR 2014), Tianjin, China.","DOI":"10.2991\/gefhr-14.2014.20"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ahmadi, A., Mitchell, E., Destelle, F., Gowing, M., O\u2019Connor, N.E., Richter, C., and Moran, K. (2014, January 16\u201319). Automatic activity classification and movement assessment during a sports training session using wearable inertial sensors. Proceedings of the 2014 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Zurich, Switzerland.","DOI":"10.1109\/BSN.2014.29"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3233\/AIS-2009-0021","article-title":"Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf","volume":"1","author":"Ghasemzadeh","year":"2009","journal-title":"J. Ambient Intell. Smart Environ."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/JSEN.2010.2048205","article-title":"Coordination analysis of human movements with body sensor networks: A signal processing model to evaluate baseball swings","volume":"11","author":"Ghasemzadeh","year":"2011","journal-title":"IEEE Sens. J."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"33","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. (CSUR)"},{"key":"ref_50","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_51","doi-asserted-by":"crossref","unstructured":"Garcia-Ceja, E., and Brena, R.F. (2016). Activity Recognition Using Community Data to Complement Small Amounts of Labeled Instances. Sensors, 16.","DOI":"10.3390\/s16060877"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Rieger, R., and Chen, S. (2006, January 14\u201317). A signal based clocking scheme for A\/D converters in body sensor networks. Proceedings of the 2006 IEEE Region 10 Conference TENCON 2006, Hong Kong, China.","DOI":"10.1109\/TENCON.2006.344049"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1109\/TNSRE.2008.2008648","article-title":"An adaptive sampling system for sensor nodes in body area networks","volume":"17","author":"Rieger","year":"2009","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1016\/j.comcom.2006.02.011","article-title":"Wireless sensor networks for personal health monitoring: Issues and an implementation","volume":"29","author":"Otto","year":"2006","journal-title":"Comput. Commun."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Von Borries, R., Pierluissi, J., and Nazeran, H. (2005, January 1\u20134). Wavelet transform-based ECG baseline drift removal for body surface potential mapping. Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, Shanghai, China.","DOI":"10.1109\/IEMBS.2005.1615311"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1007\/s00779-010-0293-9","article-title":"Preprocessing techniques for context recognition from accelerometer data","volume":"14","author":"Figo","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Huynh, T., and Schiele, B. (2005, January 12\u201314). Analyzing features for activity recognition. Proceedings of the 2005 Joint Conference on Smart Objects And Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, Grenoble, France.","DOI":"10.1145\/1107548.1107591"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guenterberg, E., Ostadabbas, S., Ghasemzadeh, H., and Jafari, R. (2009, January 1\u20133). An automatic segmentation technique in body sensor networks based on signal energy. Proceedings of the Fourth International Conference on Body Area Networks, Los Angeles, CA, USA.","DOI":"10.4108\/ICST.BODYNETS2009.6036"},{"key":"ref_59","unstructured":"Lee, C., and Xu, Y. (1996, January 22\u201328). Online, interactive learning of gestures for human\/robot interfaces. Proceedings of the 1996 IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s00779-003-0240-0","article-title":"Using GPS to learn significant locations and predict movement across multiple users","volume":"7","author":"Ashbrook","year":"2003","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1109\/10.398638","article-title":"The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition [movements classification]","volume":"42","author":"Kang","year":"1995","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Zinnen, A., Wojek, C., and Schiele, B. (2009). Multi activity recognition based on bodymodel-derived primitives. International Symposium on Location-and Context-Awareness, Springer.","DOI":"10.1007\/978-3-642-01721-6_1"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Zhang, M., and Sawchuk, A.A. (2012, January 28\u201330). Motion primitive-based human activity recognition using a bag-of-features approach. Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, Miami, FL, USA.","DOI":"10.1145\/2110363.2110433"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/S0004-3702(97)00043-X","article-title":"Wrappers for feature subset selection","volume":"97","author":"Kohavi","year":"1997","journal-title":"Artif. Intell."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Somol, P., Novovi\u010dov\u00e1, J., and Pudil, P. (2006). Flexible-hybrid sequential floating search in statistical feature selection. Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Springer.","DOI":"10.1007\/11815921_69"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM international conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Aha, D.W. (1997). Editorial. Lazy Learning, Springer.","DOI":"10.1007\/978-94-017-2053-3"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1007\/s00779-009-0277-9","article-title":"An activity monitoring system for elderly care using generative and discriminative models","volume":"14","author":"Englebienne","year":"2010","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1023\/A:1009744630224","article-title":"Automatic construction of decision trees from data: A multi-disciplinary survey","volume":"2","author":"Murthy","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Hosmer, D.W., Lemeshow, S., and Sturdivant, R.X. (2013). Applied Logistic Regression, John Wiley & Sons.","DOI":"10.1002\/9781118548387"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_74","unstructured":"Jensen, F.V. (1996). An Introduction to Bayesian Networks, UCL Press."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1109\/5326.897072","article-title":"Neural networks for classification: A survey","volume":"30","author":"Zhang","year":"2000","journal-title":"IEEE Trans. Syst. Man, Cybern. Part C Appl. Rev."},{"key":"ref_76","unstructured":"Friedman, N. (2002). Seapower as Strategy: Navies and National Interests, Naval Institute Press."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"683701","DOI":"10.1155\/2015\/683701","article-title":"A survey on multisensor fusion and consensus filtering for sensor networks","volume":"2015","author":"Li","year":"2015","journal-title":"Discret. Dyn. Nat. Soc."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00530-010-0182-0","article-title":"Multimodal fusion for multimedia analysis: A survey","volume":"16","author":"Atrey","year":"2010","journal-title":"Multimed. Syst."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/5.554205","article-title":"An introduction to multisensor data fusion","volume":"85","author":"Hall","year":"1997","journal-title":"Proc. IEEE"},{"key":"ref_80","unstructured":"Bosse, E., Roy, J., and Grenier, D. (1996, January 26\u201329). Data fusion concepts applied to a suite of dissimilar sensors. Proceedings of the 1996 Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada."},{"key":"ref_81","unstructured":"Schuldhaus, D., Leutheuser, H., and Eskofier, B.M. (October, January 29). Towards big data for activity recognition: A novel database fusion strategy. Proceedings of the 9th International Conference on Body Area Networks, London, UK."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"5406","DOI":"10.3390\/s130505406","article-title":"A survey of body sensor networks","volume":"13","author":"Lai","year":"2013","journal-title":"Sensors"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.pmcj.2013.10.007","article-title":"Tool support for detection and analysis of following and leadership behavior of pedestrians from mobile sensing data","volume":"10","author":"Blunck","year":"2014","journal-title":"Pervasive Mob. Comput."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4405","DOI":"10.1007\/s11042-015-3177-1","article-title":"A survey of depth and inertial sensor fusion for human action recognition","volume":"76","author":"Chen","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yang, G.Z., and Yang, G. (2006). Body Sensor Networks, Springer.","DOI":"10.1007\/1-84628-484-8"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ling, J., Tian, L., and Li, C. (2016). 3D human activity recognition using skeletal data from RGBD sensors. International Symposium on Visual Computing, Springer.","DOI":"10.1007\/978-3-319-50832-0_14"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/JBHI.2016.2633287","article-title":"A deep learning approach to on-node sensor data analytics for mobile or wearable devices","volume":"21","author":"Ravi","year":"2017","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Altini, M., Penders, J., and Amft, O. (2012, January 22\u201325). Energy expenditure estimation using wearable sensors: A new methodology for activity-specific models. Proceedings of the Conference on Wireless Health, La Jolla, CA, USA.","DOI":"10.1145\/2448096.2448097"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1109\/TBME.2011.2178070","article-title":"Multisensor data fusion for physical activity assessment","volume":"59","author":"Liu","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Zappi, P., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Troster, G. (2007, January 3\u20136). Activity recognition from on-body sensors by classifier fusion: sensor scalability and robustness. Proceedings of the 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, Australia.","DOI":"10.1109\/ISSNIP.2007.4496857"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A decision-theoretic generalization of on-line learning and an application to boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.3837\/tiis.2013.11.018","article-title":"A genetic algorithm-based classifier ensemble optimization for activity recognition in smart homes","volume":"7","author":"Fatima","year":"2013","journal-title":"KSII Trans. Internet Inf. Syst. (TIIS)"},{"key":"ref_93","unstructured":"Raschka, S. (2015). Python Machine Learning, Packt Publishing Ltd."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press.","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_95","unstructured":"Kluyver, T., Ragan-Kelley, B., P\u00e9rez, F., Granger, B.E., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J.B., Grout, J., and Corlay, S. (2016). Jupyter Notebooks\u2014A publishing format for reproducible computational workflows. Positioning and Power in Academic Publishing: Players, Agents and Agendas, IOS Press."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_97","unstructured":"(2019, April 29). A Simple Sequentially Rejective Multiple Test Procedure. Available online: https:\/\/www.scienceopen.com\/document?vid=2288c405-e825-4f16-9e92-97d5c305afbf."},{"key":"ref_98","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","year":"2006","journal-title":"J. Mach. Learn. Res."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Chen, C., Jafari, R., and Kehtarnavaz, N. (2015, January 27\u201330). Utd-mhad: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"ref_100","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 2012 16th International Symposium on Wearable Computers (ISWC), Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"S6","DOI":"10.1186\/1475-925X-14-S2-S6","article-title":"Design, implementation and validation of a novel open framework for agile development of mobile health applications","volume":"14","author":"Banos","year":"2015","journal-title":"Biomed. Eng. Online"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1109\/JSEN.2010.2045498","article-title":"SHIMMER\u2122\u2014A wireless sensor platform for noninvasive biomedical research","volume":"10","author":"Burns","year":"2010","journal-title":"IEEE Sens. J."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3605","DOI":"10.1016\/j.patcog.2010.04.019","article-title":"Comparative study on classifying human activities with miniature inertial and magnetic sensors","volume":"43","author":"Altun","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","article-title":"Transition-aware human activity recognition using smartphones","volume":"171","author":"Oneto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"6474","DOI":"10.3390\/s140406474","article-title":"Window size impact in human activity recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_106","unstructured":"Tan, P.N., Steinbach, M., and Kumar, V. (2005). Introduction to Data Mining, Pearson Addison-Wesley."},{"key":"ref_107","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.proeng.2012.07.166","article-title":"A comparison study of classifier algorithms for mobile-phone\u2019s accelerometer based activity recognition","volume":"41","author":"Ayu","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_109","unstructured":"Maguire, D., and Frisby, R. (2009, January 22\u201323). Comparison of feature classification algorithm for activity recognition based on accelerometer and heart rate data. Proceedings of the 9th IT & T Conference Proceedings, Dublin, Ireland."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Lee, Y.S., and Cho, S.B. (2011, January 23\u201325). Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer. Proceedings of the International Conference on Hybrid Artificial Intelligence Systems, Wroclaw, Poland.","DOI":"10.1007\/978-3-642-21219-2_58"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2017\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:48:02Z","timestamp":1760186882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/9\/2017"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,29]]},"references-count":110,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["s19092017"],"URL":"https:\/\/doi.org\/10.3390\/s19092017","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,4,29]]}}}