{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:19:52Z","timestamp":1783610392608,"version":"3.55.0"},"reference-count":176,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2019,9,3]],"date-time":"2019-09-03T00:00:00Z","timestamp":1567468800000},"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>In Ambient Intelligence (AmI), the activity a user is engaged in is an essential part of the context, so its recognition is of paramount importance for applications in areas like sports, medicine, personal safety, and so forth. The concurrent use of multiple sensors for recognition of human activities in AmI is a good practice because the information missed by one sensor can sometimes be provided by the others and many works have shown an accuracy improvement compared to single sensors. However, there are many different ways of integrating the information of each sensor and almost every author reporting sensor fusion for activity recognition uses a different variant or combination of fusion methods, so the need for clear guidelines and generalizations in sensor data integration seems evident. In this survey we review, following a classification, the many fusion methods for information acquired from sensors that have been proposed in the literature for activity recognition; we examine their relative merits, either as they are reported and sometimes even replicated and a comparison of these methods is made, as well as an assessment of the trends in the area.<\/jats:p>","DOI":"10.3390\/s19173808","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T08:28:13Z","timestamp":1567585693000},"page":"3808","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":97,"title":["Multi-Sensor Fusion for Activity Recognition\u2014A Survey"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5155-3543","authenticated-orcid":false,"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, Yucatan 97110, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oscar","family":"Mayora","sequence":"additional","affiliation":[{"name":"Fandazione Bruno Kessler Foundation, 38123 Trento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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, Yucatan 97302, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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 52926, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schilit, B.N., Adams, N., and Want, R. (1994, January 8\u20139). Context-Aware Computing Applications. Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, Santa Cruz, CA, USA.","DOI":"10.1109\/WMCSA.1994.16"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bullinger, H.J. (2009). Ambient intelligence. Technology Guide, Springer.","DOI":"10.1007\/978-3-540-88546-7"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1109\/TST.2014.6838194","article-title":"Activity recognition with smartphone sensors","volume":"19","author":"Su","year":"2014","journal-title":"Tsinghua Sci. Technol."},{"key":"ref_5","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_6","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_7","unstructured":"Witten, I.H., Frank, E., Hall, M.A., and Pal, C.J. (2016). Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22500","DOI":"10.3390\/s141222500","article-title":"Long-Term Activity Recognition from Wristwatch Accelerometer Data","volume":"14","author":"Brena","year":"2014","journal-title":"Sensors"},{"key":"ref_9","first-page":"2316757","article-title":"Energy-efficient real-time human activity recognition on smart mobile devices","volume":"2016","author":"Lee","year":"2016","journal-title":"Mob. Inf. Syst."},{"key":"ref_10","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_11","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_12","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_13","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_14","unstructured":"Jensen, F.V. (1996). An Introduction to Bayesian Networks, UCL Press."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Aha, D.W. (1997). Editorial. Lazy Learning, Springer.","DOI":"10.1007\/978-94-017-2053-3"},{"key":"ref_17","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_18","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_19","unstructured":"Zhang, L., Yang, M., and Feng, X. (2011, January 6\u201313). Sparse representation or collaborative representation: Which helps face recognition?. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Barcelona, Spain."},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"10146","DOI":"10.3390\/s140610146","article-title":"Fusion of smartphone motion sensors for physical activity recognition","volume":"14","author":"Shoaib","year":"2014","journal-title":"Sensors"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1023\/A:1007563306331","article-title":"Pasting small votes for classification in large databases and on-line","volume":"36","author":"Breiman","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_25","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_26","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_27","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_28","doi-asserted-by":"crossref","first-page":"481580","DOI":"10.1155\/2013\/481580","article-title":"Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone","volume":"9","author":"He","year":"2013","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_29","unstructured":"Gao, L., Bourke, A.K., and Nelson, J. (September, January 28). Activity recognition using dynamic multiple sensor fusion in body sensor networks. Proceedings of the 2012 Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"1692","DOI":"10.1109\/JPROC.2010.2057231","article-title":"Audiovisual information fusion in human-computer interfaces and intelligent environments: A survey","volume":"98","author":"Shivappa","year":"2010","journal-title":"Proc. IEEE"},{"key":"ref_32","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. Tutorials"},{"key":"ref_33","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"ref_34","unstructured":"Liggins, M.E., Hall, D.L., and Llinas, J. (2009). Handbook of Multisensor Data Fusion: Theory and Practice, CRC Press."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., and Cook, D.J. (2012, January 26\u201329). Simple and complex activity recognition through smart phones. Proceedings of the 8th International Conference on Intelligent Environments (IE), Guanajuato, Mexico.","DOI":"10.1109\/IE.2012.39"},{"key":"ref_37","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_38","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_39","unstructured":"Bloom, D.E., Cafiero, E., Jan\u00e9-Llopis, E., Abrahams-Gessel, S., Bloom, L.R., Fathima, S., Feigl, A.B., Gaziano, T., Hamandi, A., and Mowafi, M. (2011). The Global Economic Burden of Noncommunicable Diseases, World Economic Forum. Technical Report; Harvard School of Public Health, Program on the Global Demography of Aging."},{"key":"ref_40","unstructured":"Lorig, K., Holman, H., and Sobel, D. (2012). Living a Healthy Life with Chronic Conditions: Self-Management of Heart Disease, Arthritis, Diabetes, Depression, Asthma, Bronchitis, Emphysema and Other Physical and Mental Health Conditions, Bull Publishing Company."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"g2472","DOI":"10.1136\/bmj.g2472","article-title":"Relation of physical activity time to incident disability in community dwelling adults with or at risk of knee arthritis: Prospective cohort study","volume":"348","author":"Dunlop","year":"2014","journal-title":"BMJ"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.hrtlng.2013.04.005","article-title":"Physical activity in people with COPD, using the National Health and Nutrition Evaluation Survey dataset (2003\u20132006)","volume":"42","author":"Park","year":"2013","journal-title":"Heart Lung J. Acute Crit. Care"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/ijo.2014.115","article-title":"International study of objectively measured physical activity and sedentary time with body mass index and obesity: IPEN adult study","volume":"39","author":"Cerin","year":"2015","journal-title":"Int. J. Obes."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Morgan, W.P., and Goldston, S.E. (2013). Exercise and Mental Health, Taylor & Francis.","DOI":"10.4324\/9780203780749"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"97","DOI":"10.4258\/hir.2012.18.2.97","article-title":"Wearable sensors in healthcare and sensor-enhanced health information systems: All our tomorrows?","volume":"18","author":"Marschollek","year":"2012","journal-title":"Healthc. Inform. Res."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Van Hoof, C., and Penders, J. (2013, January 18\u201322). Addressing the healthcare cost dilemma by managing health instead of managing illness: An opportunity for wearable wireless sensors. Proceedings of the 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE), Grenoble, France.","DOI":"10.7873\/DATE.2013.312"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1377\/hlthaff.24.5.1103","article-title":"Can electronic medical record systems transform health care? Potential health benefits, savings, and costs","volume":"24","author":"Hillestad","year":"2005","journal-title":"Health Aff."},{"key":"ref_48","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_49","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_50","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 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain.","DOI":"10.1109\/ICDMW.2016.0076"},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1038\/nature06516","article-title":"The coming acceleration of global population ageing","volume":"451","author":"Lutz","year":"2008","journal-title":"Nature"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1093\/gerona\/62.7.744","article-title":"Frailty and risk of falls, fracture, and mortality in older women: The study of osteoporotic fractures","volume":"62","author":"Ensrud","year":"2007","journal-title":"J. Gerontol. Ser. A Biol. Sci. Med Sci."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"492","DOI":"10.1111\/j.1532-5415.2009.02137.x","article-title":"A comparison of frailty indexes for the prediction of falls, disability, fractures, and mortality in older men","volume":"57","author":"Ensrud","year":"2009","journal-title":"J. Am. Geriatr. Soc."},{"key":"ref_55","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 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA."},{"key":"ref_56","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 7th International Conference on Intelligent Environments (IE), Nottingham, UK.","DOI":"10.1109\/IE.2011.11"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, Q., and Stankovic, J.A. (2011, January 10\u201313). Grammar-based, posture-and context-cognitive detection for falls with different activity levels. Proceedings of the 2nd Conference on Wireless Health, San Diego, CA, USA.","DOI":"10.1145\/2077546.2077553"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1109\/JBHI.2012.2237034","article-title":"Triaxial accelerometer-based fall detection method using a self-constructing cascade-AdaBoost-SVM classifier","volume":"17","author":"Cheng","year":"2013","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_59","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_60","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 11th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Zurich, Switzerland.","DOI":"10.1109\/BSN.2014.29"},{"key":"ref_61","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_62","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 Sensors J."},{"key":"ref_63","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_64","doi-asserted-by":"crossref","unstructured":"Frontoni, E., Raspa, P., Mancini, A., Zingaretti, P., and Placidi, V. (2013, January 9\u201313). Customers\u2019 activity recognition in intelligent retail environments. Proceedings of the International Conference on Image Analysis and Processing, Naples, Italy.","DOI":"10.1007\/978-3-642-41190-8_55"},{"key":"ref_65","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_66","doi-asserted-by":"crossref","first-page":"19806","DOI":"10.3390\/s141019806","article-title":"Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors","volume":"14","author":"Delahoz","year":"2014","journal-title":"Sensors"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1016\/j.gaitpost.2009.07.128","article-title":"Real-time gait event detection using wearable sensors","volume":"30","author":"Hanlon","year":"2009","journal-title":"Gait Posture"},{"key":"ref_68","unstructured":"Mitchell, T.M. (1997). Machine Learning, McGraw-Hill."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Breiman, L. (2017). Classification and Regression Trees, Routledge.","DOI":"10.1201\/9781315139470"},{"key":"ref_70","unstructured":"Quinlan, J.R. (1993). C4. 5: Programs for Machine Learning, Morgan Kaufmann."},{"key":"ref_71","first-page":"2015","article-title":"Consistency of random forests and other averaging classifiers","volume":"9","author":"Biau","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1093\/bioinformatics\/btl074","article-title":"GALGO: An R package for multivariate variable selection using genetic algorithms","volume":"22","author":"Trevino","year":"2006","journal-title":"Bioinformatics"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10115-007-0114-2","article-title":"Top 10 algorithms in data mining","volume":"14","author":"Wu","year":"2008","journal-title":"Knowl. Inf. Syst."},{"key":"ref_74","unstructured":"Christopher, M.B. (2016). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Bishop, C.M. (1995). Neural Networks for Pattern Recognition, Oxford University Press.","DOI":"10.1093\/oso\/9780198538493.001.0001"},{"key":"ref_76","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_77","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 IEEE Region 10 Conference TENCON 2006, Hong Kong, China.","DOI":"10.1109\/TENCON.2006.344049"},{"key":"ref_78","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_79","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_80","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TPAMI.2010.86","article-title":"Eye movement analysis for activity recognition using electrooculography","volume":"33","author":"Bulling","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_81","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_82","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_83","unstructured":"Lee, C., and Xu, Y. (1996, January 22\u201328). Online, interactive learning of gestures for human\/robot interfaces. Proceedings of the IEEE International Conference on Robotics and Automation, Minneapolis, MN, USA."},{"key":"ref_84","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_85","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_86","doi-asserted-by":"crossref","unstructured":"Zinnen, A., Wojek, C., and Schiele, B. (2009, January 7\u20138). Multi activity recognition based on bodymodel-derived primitives. Proceedings of the International Symposium on Location-and Context-Awareness, Tokyo, Japan.","DOI":"10.1007\/978-3-642-01721-6_1"},{"key":"ref_87","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_88","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_89","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_90","doi-asserted-by":"crossref","unstructured":"Somol, P., Novovi\u010dov\u00e1, J., and Pudil, P. (2006, January 17\u201319). Flexible-hybrid sequential floating search in statistical feature selection. Proceedings of the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Hong Kong, China.","DOI":"10.1007\/11815921_69"},{"key":"ref_91","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_92","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_93","first-page":"10","article-title":"Seapower as Strategy: Navies and National Interests","volume":"30","author":"Friedman","year":"2002","journal-title":"Def. Foreign Aff. Strateg. Policy"},{"key":"ref_94","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_95","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_96","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 Canadian Conference on Electrical and Computer Engineering, Calgary, AB, Canada."},{"key":"ref_97","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_98","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_99","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_100","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 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, Melbourne, Australia.","DOI":"10.1109\/ISSNIP.2007.4496857"},{"key":"ref_101","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_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.infsof.2015.03.007","article-title":"Guidelines for conducting systematic mapping studies in software engineering: An update","volume":"64","author":"Petersen","year":"2015","journal-title":"Inf. Softw. Technol."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Petersen, K., Feldt, R., Mujtaba, S., and Mattsson, M. (2008, January 26\u201327). Systematic Mapping Studies in Software Engineering. Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering (EASE), Bari, Italy.","DOI":"10.14236\/ewic\/EASE2008.8"},{"key":"ref_104","unstructured":"Kitchenham, B., and Charters, S. (2007). Guidelines for Performing Systematic Literature Reviews in Software Engineering, Durham University."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1007\/s10664-008-9091-7","article-title":"Developing search strategies for detecting relevant experiments","volume":"14","author":"Dieste","year":"2009","journal-title":"Empir. Softw. Eng."},{"key":"ref_106","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_107","doi-asserted-by":"crossref","unstructured":"Kj\u00e6rgaard, M.B., and Munk, C.V. (2008, January 17\u201321). Hyperbolic location fingerprinting: A calibration-free solution for handling differences in signal strength (concise contribution). Proceedings of the Sixth Annual IEEE International Conference on Pervasive Computing and Communications (PerCom), Hong Kong, China.","DOI":"10.1109\/PERCOM.2008.75"},{"key":"ref_108","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 IEEE 18th International Workshop on Multimedia Signal Processing (MMSP), Montreal, QC, Canada.","DOI":"10.1109\/MMSP.2016.7813381"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Ling, J., Tian, L., and Li, C. (2016, January 12\u201314). 3D human activity recognition using skeletal data from RGBD sensors. Proceedings of the International Symposium on Visual Computing, Las Vegas, NV, USA.","DOI":"10.1007\/978-3-319-50832-0_14"},{"key":"ref_110","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_111","doi-asserted-by":"crossref","unstructured":"Adelsberger, R., and Tr\u00f6ster, G. (2013, January 2\u20135). Pimu: A wireless pressure-sensing imu. Proceedings of the IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Melbourne, Australia.","DOI":"10.1109\/ISSNIP.2013.6529801"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Altini, M., Penders, J., and Amft, O. (2012, January 23\u201325). Energy expenditure estimation using wearable sensors: A new methodology for activity-specific models. Proceedings of the Conference on Wireless Health, San Diego, CA, USA.","DOI":"10.1145\/2448096.2448097"},{"key":"ref_113","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_114","doi-asserted-by":"crossref","unstructured":"Libal, V., Ramabhadran, B., Mana, N., Pianesi, F., Chippendale, P., Lanz, O., and Potamianos, G. (2009, January 10\u201312). Multimodal classification of activities of daily living inside smart homes. Proceedings of the International Work-Conference on Artificial Neural Networks, Salamanca, Spain.","DOI":"10.1007\/978-3-642-02481-8_103"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Zebin, T., Scully, P.J., and Ozanyan, K.B. (2017). Inertial Sensor Based Modelling of Human Activity Classes: Feature Extraction and Multi-sensor Data Fusion Using Machine Learning Algorithms. eHealth 360, Springer.","DOI":"10.1007\/978-3-319-49655-9_38"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.patrec.2007.01.012","article-title":"Fast principal component analysis using fixed-point algorithm","volume":"28","author":"Sharma","year":"2007","journal-title":"Pattern Recognit. Lett."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1662","DOI":"10.1016\/j.eswa.2012.09.004","article-title":"Elderly activities recognition and classification for applications in assisted living","volume":"40","author":"Chernbumroong","year":"2013","journal-title":"Expert Syst. Appl."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1007\/PL00009895","article-title":"Assessing the impact of input features in a feedforward neural network","volume":"9","author":"Wang","year":"2000","journal-title":"Neural Comput. Appl."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Xiao, L., Li, R., Luo, J., and Duan, M. (2013, January 17\u201319). Activity recognition via distributed random projection and joint sparse representation in body sensor networks. Proceedings of the China Conference Wireless Sensor Networks, Qingdao, China.","DOI":"10.1007\/978-3-642-54522-1_6"},{"key":"ref_120","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_121","doi-asserted-by":"crossref","unstructured":"Bao, L., and Intille, S.S. (2004, January 21\u201323). Activity recognition from user-annotated acceleration data. Proceedings of the International Conference on Pervasive Computing, Vienna, Austria.","DOI":"10.1007\/978-3-540-24646-6_1"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/TITB.2007.899496","article-title":"Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions","volume":"12","author":"Ermes","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Alam, M.A.U., Pathak, N., and Roy, N. (2015, January 22\u201324). Mobeacon: An iBeacon-assisted smartphone-based real time activity recognition framework. Proceedings of the 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services on 12th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Coimbra, Portugal.","DOI":"10.4108\/eai.22-7-2015.2260073"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Sun, Q., and Pfahringer, B. (2012, January 4\u20137). Bagging ensemble selection for regression. Proceedings of the Australasian Joint Conference on Artificial Intelligence, Sydney, Australia.","DOI":"10.1007\/978-3-642-35101-3_59"},{"key":"ref_125","first-page":"27","article-title":"Learning the kernel matrix with semidefinite programming","volume":"5","author":"Lanckriet","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/TNNLS.2012.2237183","article-title":"Soft margin multiple kernel learning","volume":"24","author":"Xu","year":"2013","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"ref_127","first-page":"2491","article-title":"SimpleMKL","volume":"9","author":"Rakotomamonjy","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Guo, H., Chen, L., Shen, Y., and Chen, G. (2014, January 13\u201317). Activity recognition exploiting classifier level fusion of acceleration and physiological signals. Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing: Adjunct publication, Seattle, WA, USA.","DOI":"10.1145\/2638728.2638777"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","article-title":"A survey of multi-view machine learning","volume":"23","author":"Sun","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: Recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0893-6080(05)80023-1","article-title":"Stacked generalization","volume":"5","author":"Wolpert","year":"1992","journal-title":"Neural Netw."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Gao, L., Bourke, A.K., and Nelson, J. (2011, January 27\u201329). An efficient sensing approach using dynamic multi-sensor collaboration for activity recognition. Proceedings of the International Conference on Distributed Computing in Sensor Systems and Workshops (DCOSS), Barcelona, Spain.","DOI":"10.1109\/DCOSS.2011.5982190"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Aly, H., and Ismail, M.A. (2015, January 13\u201317). ubiMonitor: Intelligent fusion of body-worn sensors for real-time human activity recognition. Proceedings of the 30th Annual ACM Symposium on Applied Computing, Salamanca, Spain.","DOI":"10.1145\/2695664.2695912"},{"key":"ref_134","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_135","doi-asserted-by":"crossref","unstructured":"Arnon, P. (2014, January 2\u20134). Classification model for multi-sensor data fusion apply for Human Activity Recognition. Proceedings of the International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, Malaysia.","DOI":"10.1109\/I4CT.2014.6914217"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s12193-014-0149-0","article-title":"Combination of sequential class distributions from multiple channels using Markov fusion networks","volume":"8","author":"Glodek","year":"2014","journal-title":"J. Multimodal User Interfaces"},{"key":"ref_137","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_138","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/JBHI.2014.2313473","article-title":"Genetic algorithm-based classifiers fusion for multisensor activity recognition of elderly people","volume":"19","author":"Chernbumroong","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1250084","DOI":"10.1142\/S0219519412500844","article-title":"Human activity recognition by fusing multiple sensor nodes in the wearable sensor systems","volume":"12","author":"Guo","year":"2012","journal-title":"J. Mech. Med. Biol."},{"key":"ref_140","first-page":"211","article-title":"Sparse Bayesian learning and the relevance vector machine","volume":"1","author":"Tipping","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Grokop, L.H., Sarah, A., Brunner, C., Narayanan, V., and Nanda, S. (2011, January 17\u201321). Activity and device position recognition in mobile devices. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China.","DOI":"10.1145\/2030112.2030228"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1109\/TSMCA.2010.2093883","article-title":"Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living","volume":"41","author":"Zhu","year":"2011","journal-title":"IEEE Trans. Syst. Man, Cybern. Part A Syst. Humans"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Liu, R., and Liu, M. (2010, January 18\u201320). Recognizing human activities based on multi-sensors fusion. Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), Chengdu, China.","DOI":"10.1109\/ICBBE.2010.5514802"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1109\/TNSRE.2010.2053217","article-title":"Multimodal physical activity recognition by fusing temporal and cepstral information","volume":"18","author":"Li","year":"2010","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_145","unstructured":"Ross, A.A., Nandakumar, K., and Jain, A.K. (2006). Handbook of Multibiometrics, Springer Science & Business Media."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"2564","DOI":"10.1109\/TIP.2010.2052823","article-title":"Tracking and activity recognition through consensus in distributed camera networks","volume":"19","author":"Song","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_147","unstructured":"Lester, J., Choudhury, T., Kern, N., Borriello, G., and Hannaford, B. (August, January 30). A hybrid discriminative\/generative approach for modeling human activities. Proceedings of the 19th International Joint Conference on Artificial Intelligence, Edinburgh, UK."},{"key":"ref_148","first-page":"511","article-title":"Rapid object detection using a boosted cascade of simple features","volume":"1","author":"Viola","year":"2001","journal-title":"CVPR (1)"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1023\/A:1022631118932","article-title":"Very simple classification rules perform well on most commonly used datasets","volume":"11","author":"Holte","year":"1993","journal-title":"Mach. Learn."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"315","DOI":"10.4103\/0976-500X.85931","article-title":"Measures of dispersion","volume":"2","author":"Manikandan","year":"2011","journal-title":"J. Pharmacol. Pharmacother."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable assistant for Parkinson\u2019s disease patients with the freezing of gait symptom","volume":"14","author":"Bachlin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1109\/THMS.2014.2377111","article-title":"Human activity recognition process using 3-D posture data","volume":"45","author":"Gaglio","year":"2015","journal-title":"IEEE Trans. Hum. Mach. Syst."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Kr\u00f6se, B., Van Kasteren, T., Gibson, C., and Van den Dool, T. (2008, January 4\u20136). Care: Context awareness in residences for elderly. Proceedings of the International Conference of the International Society for Gerontechnology, Pisa, Italy.","DOI":"10.4017\/gt.2008.07.02.083.00"},{"key":"ref_155","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":"ACM SIGKDD Explor. Newsl."},{"key":"ref_156","unstructured":"iBeacon Team (2019, March 29). Estimote iBeacon. Available online: https:\/\/estimote.com."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Guo, H., Chen, L., Peng, L., and Chen, G. (2016, January 12\u201316). Wearable sensor based multimodal human activity recognition exploiting the diversity of classifier ensemble. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg, Germany.","DOI":"10.1145\/2971648.2971708"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"1018","DOI":"10.1016\/j.asoc.2015.01.025","article-title":"On the use of ensemble of classifiers for accelerometer-based activity recognition","volume":"37","author":"Catal","year":"2015","journal-title":"Appl. Soft Comput."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Kalu\u017ea, B., Mirchevska, V., Dovgan, E., Lu\u0161trek, M., and Gams, M. (2010, January 10\u201312). An agent-based approach to care in independent living. Proceedings of the International Joint Conference on Ambient Intelligence, Malaga, Spain.","DOI":"10.1007\/978-3-642-16917-5_18"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Ravi, D., Wong, C., Lo, B., and Yang, G.Z. (2016, January 14\u201317). Deep learning for human activity recognition: A resource efficient implementation on low-power devices. Proceedings of the IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516235"},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., and Pulickal, T.T. (2011, January 21). Design considerations for the WISDM smart phone-based sensor mining architecture. Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA, USA.","DOI":"10.1145\/2003653.2003656"},{"key":"ref_162","doi-asserted-by":"crossref","unstructured":"Zappi, P., Lombriser, C., Stiefmeier, T., Farella, E., Roggen, D., Benini, L., and Tr\u00f6ster, G. (2008). Activity recognition from on-body sensors: accuracy-power trade-off by dynamic sensor selection. Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-540-77690-1_2"},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Seidenari, L., Varano, V., Berretti, S., Del Bimbo, A., and Pala, P. (2013, January 23\u201328). Recognizing actions from depth cameras as weakly aligned multi-part bag-of-poses. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Portland, OR, USA.","DOI":"10.1109\/CVPRW.2013.77"},{"key":"ref_164","unstructured":"Cappelletti, A., Lepri, B., Mana, N., Pianesi, F., and Zancanaro, M. (2008, January 26\u201330). A multimodal data collection of daily activities in a real instrumented apartment. Proceedings of the Workshop Multimodal Corpora: From Models of Natural Interaction to Systems and Applications (LREC\u201908), Marrakech, Morocco."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Kumar, J., Li, Q., Kyal, S., Bernal, E.A., and Bala, R. (2015, January 7\u201312). On-the-fly hand detection training with application in egocentric action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301344"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"103","DOI":"10.3233\/AIS-2009-0016","article-title":"Distributed recognition of human actions using wearable motion sensor networks","volume":"1","author":"Yang","year":"2009","journal-title":"J. Ambient. Intell. Smart Environ."},{"key":"ref_167","doi-asserted-by":"crossref","unstructured":"Banos, O., Garcia, R., Holgado-Terriza, J.A., Damas, M., Pomares, H., Rojas, I., Saez, A., and Villalonga, C. (2014, January 2\u20135). mHealthDroid: A novel framework for agile development of mobile health applications. Proceedings of the International Workshop on Ambient Assisted Living, Belfast, UK.","DOI":"10.1007\/978-3-319-13105-4_14"},{"key":"ref_168","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 16th International Symposium on Wearable Computers (ISWC), Newcastle, UK.","DOI":"10.1109\/ISWC.2012.13"},{"key":"ref_169","unstructured":"Cook, D.J. (2019, April 05). CASAS Smart Home Project. Available online: http:\/\/www.ailab.wsu.edu\/casas\/."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., and Bajcsy, R. (2013, January 15\u201317). Berkeley MHAD: A comprehensive multimodal human action database. Proceedings of the IEEE Workshop on Applications of Computer Vision (WACV), Tampa, FL, USA.","DOI":"10.1109\/WACV.2013.6474999"},{"key":"ref_171","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 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7350781"},{"key":"ref_172","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 Seventh International Conference on Networked Sensing Systems (INSS), Kassel, Germany.","DOI":"10.1109\/INSS.2010.5573462"},{"key":"ref_173","doi-asserted-by":"crossref","unstructured":"Weinland, D., Boyer, E., and Ronfard, R. (2007, January 14\u201320). Action recognition from arbitrary views using 3d exemplars. Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV 2007), Rio de Janeiro, Brazil.","DOI":"10.1109\/ICCV.2007.4408849"},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"Goodwin, M.S., Haghighi, M., Tang, Q., Akcakaya, M., Erdogmus, D., and Intille, S. (2014, January 13\u201317). Moving towards a real-time system for automatically recognizing stereotypical motor movements in individuals on the autism spectrum using wireless accelerometry. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Seattle, WA, USA.","DOI":"10.1145\/2632048.2632096"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Aguileta, A.A., Brena, R.F., Mayora, O., Molino-Minero-Re, E., and Trejo, L.A. (2019). Virtual Sensors for Optimal Integration of Human Activity Data. Sensors, 19.","DOI":"10.3390\/s19092017"},{"key":"ref_176","first-page":"75","article-title":"Software Engineering Research in Mexico: A Systematic","volume":"10","author":"Aguileta","year":"2016","journal-title":"Int. J. Softw. Eng. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3808\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:16:28Z","timestamp":1760188588000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/17\/3808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,3]]},"references-count":176,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2019,9]]}},"alternative-id":["s19173808"],"URL":"https:\/\/doi.org\/10.3390\/s19173808","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,3]]}}}