{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:30Z","timestamp":1760242890204,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,10,26]],"date-time":"2016-10-26T00:00:00Z","timestamp":1477440000000},"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 recent years, smart phones with inbuilt sensors have become popular devices to facilitate activity recognition. The sensors capture a large amount of data, containing meaningful events, in a short period of time. The change points in this data are used to specify transitions to distinct events and can be used in various scenarios such as identifying change in a patient\u2019s vital signs in the medical domain or requesting activity labels for generating real-world labeled activity datasets. Our work focuses on change-point detection to identify a transition from one activity to another. Within this paper, we extend our previous work on multivariate exponentially weighted moving average (MEWMA) algorithm by using a genetic algorithm (GA) to identify the optimal set of parameters for online change-point detection. The proposed technique finds the maximum accuracy and F_measure by optimizing the different parameters of the MEWMA, which subsequently identifies the exact location of the change point from an existing activity to a new one. Optimal parameter selection facilitates an algorithm to detect accurate change points and minimize false alarms. Results have been evaluated based on two real datasets of accelerometer data collected from a set of different activities from two users, with a high degree of accuracy from 99.4% to 99.8% and F_measure of up to 66.7%.<\/jats:p>","DOI":"10.3390\/s16111784","type":"journal-article","created":{"date-parts":[[2016,10,26]],"date-time":"2016-10-26T05:47:55Z","timestamp":1477460875000},"page":"1784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Optimal Parameter Exploration for Online Change-Point Detection in Activity Monitoring Using Genetic Algorithms"],"prefix":"10.3390","volume":"16","author":[{"given":"Naveed","family":"Khan","sequence":"first","affiliation":[{"name":"School of Computing and Information Engineering, Ulster University, Coleraine, Co., Londonderry BTT52 1SA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sally","family":"McClean","sequence":"additional","affiliation":[{"name":"School of Computing and Information Engineering, Ulster University, Coleraine, Co., Londonderry BTT52 1SA, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, Ulster University, Jordanstown, Co., Antrim BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0882-7902","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, Ulster University, Jordanstown, Co., Antrim BT37 0QB, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,26]]},"reference":[{"key":"ref_1","unstructured":"Subramaniam, S., Palpanas, T., Papadopoulos, D., Kalogeraki, V., and Gunopulos, D. (2006, January 12\u201315). Online outlier detection in sensor data using non-parametric models. Proceedings of the 32nd International Conference on very Large Data Bases, Seoul, Korea."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/2.895117","article-title":"Smart dust: Communicating with a cubic-millimeter computer","volume":"34","author":"Warneke","year":"2001","journal-title":"Computer"},{"key":"ref_3","unstructured":"Behera, A., Hogg, D.C., and Cohn, A.G. (2012). Computer Vision\u2013ACCV 2012, Springer."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MIS.2008.18","article-title":"Activity recognition for the smart hospital","volume":"23","author":"Tentori","year":"2008","journal-title":"IEEE Intell. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Schaupp, K., and Eder-Halbedl, W. (2008, January 9\u201311). An Investigation on Acceptance of Ubiquitous Devices for the Elderly in a Geriatric Hospital Environment: Using the Example of Person Tracking. Proceedings of the International Conference on Computers for Handicapped Persons, Linz, Austria.","DOI":"10.1007\/978-3-540-70540-6_3"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/978-3-319-03092-0_2","article-title":"Mobile based prompted labeling of large scale activity data","volume":"Volume 8277","author":"Nugent","year":"2013","journal-title":"Ambient Assisted Living and Active Aging"},{"key":"ref_7","unstructured":"Basseville, M., and Nikiforov, I.V. (1993). Detection of Abrupt Changes: Theory and Application, Prentice Hall."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2521","DOI":"10.1109\/TPAMI.2011.36","article-title":"Weakly supervised recognition of daily life activities with wearable sensors","volume":"33","author":"Stikic","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","first-page":"99","article-title":"Nonparametric methods in change-point problems: A general approach and some concrete algorithms","volume":"23","author":"Carlstein","year":"1994","journal-title":"Change-Point Problems"},{"key":"ref_10","unstructured":"Wechsler, S.S.H.A.H. Online Change Detection. Available online: http:\/\/www.Ntu.Edu.Sg\/home\/ssho\/cpbook\/chapter5.Pdf."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Khan, N., McClean, S., Zhang, S., and Nugent, C. (2015, January 1\u20134). Parameter optimization for online change detection in activity monitoring using multivariate exponentially weighted moving average (MEWMA). Proceedings of the Ubiquitous Computing and Ambient Intelligence. Sensing, Processing, and Using Environmental Information, Puerto Varas, Chile.","DOI":"10.1007\/978-3-319-26401-1_5"},{"key":"ref_12","first-page":"39","article-title":"Genetic algorithms: Concepts, design for optimization of process controllers","volume":"4","author":"Malhotra","year":"2011","journal-title":"Comput. Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"17292","DOI":"10.3390\/s131217292","article-title":"Mobile sensing systems","volume":"13","author":"Macias","year":"2013","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"23361","DOI":"10.3390\/s150923361","article-title":"A self-adaptive behavior-aware recruitment scheme for participatory sensing","volume":"15","author":"Zeng","year":"2015","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/2808198","article-title":"Launching an efficient participatory sensing campaign: A smart mobile device-based approach","volume":"12","author":"Hao","year":"2015","journal-title":"ACM Trans. Multimed. Comput. Commun. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chang, Y.-J., Paruthi, G., and Newman, M.W. (2015, January 7\u201311). A field study comparing approaches to collecting annotated activity data in real-world settings. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Osaka, Japan.","DOI":"10.1145\/2750858.2807524"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, S., McClean, S., Scotney, B., Galway, L., and Nugent, C. (2011, January 27\u201330). A Framework for Context-Aware Online Physiological Monitoring. Proceedings of the 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), Bristol, UK.","DOI":"10.1109\/CBMS.2011.5999038"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xi, C., Chen, K.S., Boriah, S., Chatterjee, S., and Kumar, V. Contextual Time Series Change Detection. Available online: http:\/\/www-users.Cs.Umn.Edu\/~sboriah\/pdfs\/chencsbck2013.Pdf.","DOI":"10.1137\/1.9781611972832.56"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1590\/S0101-74382011000200002","article-title":"A fuzzy\/bayesian approach for the time series change point detection problem","volume":"31","author":"Palhares","year":"2011","journal-title":"Pesqui. Oper."},{"key":"ref_20","unstructured":"Vlasveld, R. (2014). Temporal Segmentation Using Support Vector Machines in the Context of Human Activity Recognition. [Master\u2019s Thesis, Thompson Rivers University]."},{"key":"ref_21","first-page":"213","article-title":"Classifying transition behaviour in postural activity monitoring","volume":"7","author":"Brusey","year":"2009","journal-title":"Sens. Transducers"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kawahara, Y., and Sugiyama, M. (May, January 30). Change-point detection in time-series data by direct density-ratio estimation. Proceedings of the 2009 SIAM International Conference on Data Mining, Sparks, NV, USA.","DOI":"10.1137\/1.9781611972795.34"},{"key":"ref_23","unstructured":"Sugiyama, M., Nakajima, S., Kashima, H., Buenau, P.V., and Kawanabe, M. (2008). Advances in Neural Information Processing Systems, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"46","DOI":"10.2307\/1269551","article-title":"A multivariate exponentially weighted moving average control chart","volume":"34","author":"Lowry","year":"1992","journal-title":"Technometrics"},{"key":"ref_25","first-page":"43","article-title":"An extension for the univariate exponentially weighted moving average control chart","volume":"20","author":"Khoo","year":"2004","journal-title":"Matematika"},{"key":"ref_26","unstructured":"Critical Values and p Values, Available online: http:\/\/www.itl.nist.gov\/div898\/handbook\/prc\/section1\/prc131.htm."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Holzinger, K., Palade, V., Rabadan, R., and Holzinger, A. (2014). Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, Springer.","DOI":"10.1007\/978-3-662-43968-5"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1022602019183","article-title":"Genetic algorithms and machine learning","volume":"3","author":"Goldberg","year":"1988","journal-title":"Mach. Learn."},{"key":"ref_29","unstructured":"Mitchell, T.M. (1997). Machine Learning, WCB\/McGraw-Hill."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.cam.2004.07.034","article-title":"Genetic algorithms for modelling and optimisation","volume":"184","author":"McCall","year":"2005","journal-title":"J. Comput. Appl. Math."},{"key":"ref_31","unstructured":"Algosnap. Available online: http:\/\/algosnap.com\/."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16181","DOI":"10.3390\/s140916181","article-title":"A lightweight hierarchical activity recognition framework using smartphone sensors","volume":"14","author":"Han","year":"2014","journal-title":"Sensors"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ziefle, M., Klack, L., Wilkowska, W., and Holzinger, A. (2013, January 21\u201326). Acceptance of telemedical treatments\u2014A medical professional point of view. Proceedings of the International Conference on Human Interface and the Management of Information, San Francisco, CA, USA.","DOI":"10.1007\/978-3-642-39215-3_39"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Khan, N., McClean, S., Zhang, S., and Nugent, C. (2016, January 21\u201323). Using genetic algorithms for optimal change point detection in activity monitoring. Proceedings of the IEEE 29th International Symposium on Computer-Based Medical Systems, Dublin-Belfast, UK.","DOI":"10.1109\/CBMS.2016.27"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"11312","DOI":"10.3390\/s150511312","article-title":"The elderly\u2019s independent living in smart homes: A characterization of activities and sensing infrastructure survey to facilitate services development","volume":"15","author":"Ni","year":"2015","journal-title":"Sensors"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. 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