{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:27:19Z","timestamp":1777397239744,"version":"3.51.4"},"reference-count":102,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2013,12,17]],"date-time":"2013-12-17T00:00:00Z","timestamp":1387238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.<\/jats:p>","DOI":"10.3390\/s131217472","type":"journal-article","created":{"date-parts":[[2013,12,17]],"date-time":"2013-12-17T12:13:31Z","timestamp":1387282411000},"page":"17472-17500","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":368,"title":["Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges"],"prefix":"10.3390","volume":"13","author":[{"given":"Hadi","family":"Banaee","sequence":"first","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-70182 \u00d6rebro, Sweden"}]},{"given":"Mobyen","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-70182 \u00d6rebro, Sweden"}]},{"given":"Amy","family":"Loutfi","sequence":"additional","affiliation":[{"name":"Center for Applied Autonomous Sensor Systems, \u00d6rebro University, SE-70182 \u00d6rebro, Sweden"}]}],"member":"1968","published-online":{"date-parts":[[2013,12,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2013). Managing and Mining Sensor Data, Springer.","DOI":"10.1007\/978-1-4614-6309-2"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s10916-011-9733-y","article-title":"A remote patient monitoring system for congestive heart failure","volume":"35","author":"Suh","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1016\/j.eswa.2010.08.075","article-title":"Development of remote healthcare system for measuring and promoting healthy lifestyle","volume":"38","author":"Youm","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1109\/JSEN.2010.2091719","article-title":"A Zigbee-based wearable physiological parameters monitoring system","volume":"12","author":"Malhi","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yamada, I., and Lopez, G. (2012, January 12\u201314). Wearable Sensing Systems for Healthcare Monitoring. Honolulu, HI, USA.","DOI":"10.1109\/VLSIT.2012.6242435"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-010-0260-8","article-title":"Body area networks: A survey","volume":"16","author":"Chen","year":"2011","journal-title":"Mob. Netw. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"13907","DOI":"10.3390\/s121013907","article-title":"A review on architectures and communications technologies for wearable health-monitoring systems","volume":"12","author":"Custodio","year":"2012","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TSMCC.2009.2032660","article-title":"A survey on wearable sensor-based systems for health monitoring and prognosis","volume":"40","author":"Pantelopoulos","year":"2010","journal-title":"Trans. Syst. Man Cyber. Part C"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Alemdar, H., and Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Comput. Netw., 2688\u20132710.","DOI":"10.1016\/j.comnet.2010.05.003"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-012-9898-z","article-title":"Smart health monitoring systems: An overview of design and modeling","volume":"37","author":"Baig","year":"2013","journal-title":"J. Med. Syst."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Atallah, L., Lo, B., and Yang, G.Z. (2012). Can pervasive sensing address current challenges in global healthcare?. J. Epidemiol. Glob. Health, 1\u201313.","DOI":"10.1016\/j.jegh.2011.11.005"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1109\/SURV.2012.110112.00192","article-title":"A survey on ambient-assisted living tools for older adults","volume":"15","author":"Lara","year":"2013","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_14","unstructured":"Avci, A., Bosch, S., Marin-Perianu, M., Marin-Perianu, R., and Havinga, P. (2010, January 22\u201323). Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey. Hannover, Germany."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.3390\/s100201154","article-title":"Machine learning methods for classifying human physical activity from on-body accelerometers","volume":"10","author":"Mannini","year":"2010","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Patel, S., Park, H., Bonato, P., Chan, L., and Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil., 1\u201317.","DOI":"10.1186\/1743-0003-9-21"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.artmed.2012.09.003","article-title":"Smart wearable systems: Current status and future challenges","volume":"56","author":"Chan","year":"2012","journal-title":"Artif. Intell. Med."},{"key":"ref_18","first-page":"416","article-title":"Predictive data mining in clinical medicine: A focus on selected methods and applications","volume":"1","author":"Bellazzi","year":"2011","journal-title":"Wiley. Interdiscip. Rev.: Data. Min. Knowl. Discov."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/cc9208","article-title":"Health technology assessment review: Remote monitoring of vital signs\u2014current status and future challenges","volume":"14","author":"Nangalia","year":"2010","journal-title":"Crit. Care"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2431","DOI":"10.1007\/s10916-011-9710-5","article-title":"Data mining in healthcare and biomedicine: A survey of the literature","volume":"36","author":"Yoo","year":"2012","journal-title":"J. Med. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.artmed.2006.08.002","article-title":"Temporal abstraction in intelligent clinical data analysis: A survey","volume":"39","author":"Stacey","year":"2007","journal-title":"Artif. Intell. Med."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mukherjee, A., Pal, A., and Misra, P. (2012, January 12\u201314). Data Analytics in Ubiquitous Sensor-Based Health Information Systems. Paris, France.","DOI":"10.1109\/NGMAST.2012.39"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Dutta, K., Xie, H.Q., Byun, J., Pottathil, A., and Moore, M. (2013, January 7\u201310). Persuasive and Pervasive Sensing: A New Frontier to Monitor, Track and Assist Older Adults Suffering from Type-2 Diabetes. Grand Wailea, Maui, HI, USA.","DOI":"10.1109\/HICSS.2013.618"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"15:1","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1109\/TKDE.2010.235","article-title":"Anomaly detection for discrete sequences: A survey","volume":"24","author":"Chandola","year":"2012","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gaura, E., Kemp, J., and Brusey, J. (2013). Leveraging knowledge from physiological data: On-body heat stress risk prediction with sensor networks. IEEE Trans. Biomed. Circuits Syst., in press.","DOI":"10.1109\/TBCAS.2013.2254485"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s11265-012-0672-8","article-title":"Low-energy formulations of support vector machine kernel functions for biomedical sensor applications","volume":"69","author":"Lee","year":"2012","journal-title":"J. Signal Process. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1109\/JCN.2011.6157411","article-title":"Automatic detection of anomalies in blood glucose using a machine learning approach","volume":"13","author":"Zhu","year":"2011","journal-title":"J. Commun. Netw."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1007\/978-3-642-29734-2_35","article-title":"Identifying Chronic Disease Complications Utilizing State of the Art Data Fusion Methodologies and Signal Processing Algorithms","volume":"Volume 83","author":"Nikita","year":"2012","journal-title":"Wireless Mobile Communication and Healthcare"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Zhang, Y., Cao, J., Steyn, M., and Taraporewalla, K. (2013). Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web.","DOI":"10.1007\/s11280-013-0203-y"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1109\/TBME.2012.2208459","article-title":"Gaussian processes for personalized e-health monitoring with wearable sensors","volume":"60","author":"Clifton","year":"2013","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_32","first-page":"13","article-title":"Support vector machine for abnormal pulse classification","volume":"22","author":"Thakker","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1002\/acs.1123","article-title":"On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units","volume":"24","author":"Charbonnier","year":"2009","journal-title":"Int. J. Adapt. Control. Signal. Process."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"6897","DOI":"10.3390\/s90906897","article-title":"Detecting specific health-related events using an integrated sensor system for vital sign monitoring","volume":"9","author":"Adnane","year":"2009","journal-title":"Sensors"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Adnane, M., Jiang, Z., Mori, N., and Matsumoto, Y. (2011, January 9\u201311). An Automated Program for Mental Stress and Apnea\/Hypopnea Events Detection. Tipaza, Algeria.","DOI":"10.1109\/WOSSPA.2011.5931412"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Singh, R.R., Conjeti, S., and Banerjee, R. (2011, January 5\u2013). An Approach for Real-Time Stress-Trend Detection Using Physiological Signals in Wearable Computing Systems for Automotive Drivers. Washington, DC, USA.","DOI":"10.1109\/ITSC.2011.6082900"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ijmedinf.2006.11.006","article-title":"Predictive data mining in clinical medicine: Current issues and guidelines","volume":"77","author":"Bellazzi","year":"2008","journal-title":"Int. J. Med. Inform."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1007\/978-3-642-38622-0_21","article-title":"Experimental Study of the Stress Level at the Workplace Using an Smart Testbed of Wireless Sensor Networks and Ambient Intelligence Techniques","volume":"Volume 7931","year":"2013","journal-title":"Natural and Artificial Computation in Engineering and Medical Applications"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/978-3-642-29336-8_12","article-title":"Activity-Aware Mental Stress Detection Using Physiological Sensors","volume":"Volume 76","author":"Gris","year":"2012","journal-title":"Mobile Computing, Applications, and Services"},{"key":"ref_40","unstructured":"Marlin, B.M., Kale, D.C., Khemani, R.G., and Wetzel, R.C. (, January January). Unsupervised Pattern Discovery in Electronic Health Care Data Using Probabilistic Clustering Models. Miami, FL, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.dss.2010.11.001","article-title":"Using data mining techniques to predict hospitalization of hemodialysis patients","volume":"50","author":"Yeh","year":"2011","journal-title":"Decis. Support Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1109\/TITB.2011.2169804","article-title":"Development and evaluation of an ambulatory stress monitor based on wearable sensors","volume":"16","author":"Choi","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1016\/j.dss.2008.11.014","article-title":"Enabling ubiquitous patient monitoring: Model, decision protocols, opportunities and challenges","volume":"46","author":"Sneha","year":"2009","journal-title":"Decis. Support Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1007\/978-3-642-29734-2_36","article-title":"A Support Vector Machine Approach for Categorization of Patients Suffering from Chronic Diseases","volume":"Volume 83","author":"Nikita","year":"2012","journal-title":"Wireless Mobile Communication and Healthcare"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.knosys.2012.08.011","article-title":"Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform","volume":"37","author":"Giri","year":"2013","journal-title":"Know. Based Syst."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bellos, C., Papadopoulos, A., Rosso, R., and Fotiadis, D.I. (2012, January 5\u20137). Categorization of Patients' Health Status in Copd Disease Using a Wearable Platform and Random Forests Methodology. Shenzhen, China.","DOI":"10.1109\/BHI.2012.6211600"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"741","DOI":"10.1109\/TITB.2010.2049025","article-title":"Processing of signals recorded through smart devices: Sleep-quality assessment","volume":"14","author":"Bianchi","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1109\/TITB.2012.2188299","article-title":"Real-time sleep apnea detection by classifier combination","volume":"16","author":"Xie","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1016\/j.jss.2010.05.074","article-title":"Online discovery of Heart Rate Variability patterns in mobile healthcare services","volume":"83","author":"Vu","year":"2010","journal-title":"J. Syst. Softw."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1109\/TITB.2010.2040085","article-title":"Prognosis\u2014a wearable health-monitoring system for people at risk: Methodology and modeling","volume":"14","author":"Pantelopoulos","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1109\/TITB.2009.2038481","article-title":"On the classification of emotional biosignals evoked while viewing affective pictures: An integrated data-mining-based approach for healthcare applications","volume":"14","author":"Frantzidis","year":"2010","journal-title":"Trans. Inf. Tech. Biomed."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/TBCAS.2008.2008817","article-title":"Sleep and wake classification with ECG and respiratory effort signals","volume":"3","author":"Karlen","year":"2009","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Naraharisetti, K.V.P., Bawa, M., and Tahernezhadi, M. (2011, January 15\u201317). Comparison of Different Signal Processing Methods for Reducing Artifacts from Photoplethysmograph Signal. Mankato, MN, USA.","DOI":"10.1109\/EIT.2011.5978571"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Ding, H., Sun, H., and mean Hou, K. (2011, January 9\u201311). Abnormal ECG Signal Detection Based on Compressed Sampling in Wearable ECG Sensor. Nanjing, China.","DOI":"10.1109\/WCSP.2011.6096677"},{"key":"ref_55","unstructured":"Yoon, J. (2013, January 23\u201328). Three-Tiered Data Mining for Big Data Patterns of Wireless Sensor Networks in Medical and Healthcare Domains. Rome, Italy."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ahmad, N.F., Hoang, D.B., and Phung, M.H. (2009, January 16\u201318). Robust Preprocessing for Health Care Monitoring Framework. Sydney, Australia.","DOI":"10.1109\/HEALTH.2009.5406196"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1109\/JSYST.2011.2165597","article-title":"Secure stochastic ECG signals based on gaussian mixture model for e-healthcare systems","volume":"5","author":"Wang","year":"2011","journal-title":"IEEE Syst. J."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"F3","DOI":"10.1093\/eurheartj\/sum030","article-title":"Heart rate: An independent risk factor in cardiovascular disease","volume":"9","author":"Hjalmarson","year":"2007","journal-title":"Eur. Heart J. Suppl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1097\/mss.0b013e31803349c6","article-title":"Longitudinal modeling of the relationship between age and maximal heart rate","volume":"39","author":"Gellish","year":"2007","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Mao, Y., Chen, W., Chen, Y., Lu, C., Kollef, M., and Bailey, T. (2012, January 16\u201318). An Integrated Data Mining Approach to Real-Time Clinical Monitoring and Deterioration Warning. Beijing, China.","DOI":"10.1145\/2339530.2339709"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TITB.2008.2010702","article-title":"Real-time analysis of physiological data to support medical applications","volume":"13","author":"Apiletti","year":"2009","journal-title":"Trans. Info. Tech. Biomed."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"986","DOI":"10.1016\/j.adhoc.2007.09.002","article-title":"medical ad hoc sensor networks (MASN) with wavelet-based ECG data mining","volume":"6","author":"Hu","year":"2008","journal-title":"Ad Hoc Robust Netw."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Guyon, I., Gunn, S., Nikravesh, M., and Zadeh, L.A. (2006). Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing), Springer.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bellos, C.C., Papadopoulos, A., Rosso, R., and Fotiadis, D.I. (2010, January 3\u20135). Extraction and Analysis of Features Acquired By Wearable Sensors Network. Corfu, Greece.","DOI":"10.1109\/ITAB.2010.5687761"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1109\/TITB.2010.2087386","article-title":"Apnea medassist: Real-time sleep apnea monitor using single-lead ECG","volume":"15","author":"Bsoul","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.eswa.2006.04.020","article-title":"Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors","volume":"33","author":"Widodo","year":"2007","journal-title":"Expert Syst. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Li, X., and Porikli, F. (2010, January 23\u201326). Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis. Istanbul, Turkey.","DOI":"10.1109\/ICPR.2010.602"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Pradhan, G.N., Chattopadhyay, R., and Panchanathan, S. (2010, January 29\u201331). Processing Body Sensor Data Streams for Continuous Physiological Monitoring. Philadelphia, PA, USA.","DOI":"10.1145\/1743384.1743468"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.eswa.2007.10.005","article-title":"Neural networks and statistical techniques: A review of applications","volume":"36","author":"Paliwal","year":"2009","journal-title":"Expert. Syst. Appl."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"47","DOI":"10.2478\/v10136-012-0031-x","article-title":"Artificial neural networks in medical diagnosis","volume":"11","author":"Amato","year":"2013","journal-title":"J Appl. Biomed."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.1088\/0967-3334\/33\/9\/1491","article-title":"Dynamic time warping and machine learning for signal quality assessment of pulsatile signals","volume":"33","author":"Li","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.amjmed.2005.07.053","article-title":"Current evidence regarding the value of self-monitored blood glucose testing","volume":"118","author":"Blonde","year":"2005","journal-title":"Am. J. Med."},{"key":"ref_74","unstructured":"Jin, Z., Sun, Y., and Cheng, A.C. (2009, January 3\u20136). Predicting Cardiovascular Disease From Real-Time Electrocardiographic Monitoring: An Adaptive Machine Learning Approach on a Cell Phone."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ordonez, P., Armstrong, T., Oates, T., and Fackler, J. (2011, January 18\u201321). Classification of Patients Using Novel Multivariate Time Series Representations of Physiological Data. Honolulu, HI, USA.","DOI":"10.1109\/ICMLA.2011.46"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1023\/A:1016409317640","article-title":"Decision trees: An overview and their use in medicine","volume":"26","author":"Podgorelec","year":"2002","journal-title":"J. Med. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"11782","DOI":"10.1016\/j.eswa.2012.04.073","article-title":"Improving medical decision trees by combining relevant health-care criteria","volume":"39","author":"Bohada","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MASSP.1986.1165342","article-title":"An introduction to hidden Markov models","volume":"3","author":"Rabiner","year":"1986","journal-title":"IEEE ASSP Mag."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.pmcj.2010.01.002","article-title":"Wearable sensor activity analysis using semi-Markov models with a grammar","volume":"6","author":"Thomas","year":"2010","journal-title":"Pervasive Mob. Comput."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.mechatronics.2011.03.003","article-title":"Gait phase analysis based on a Hidden Markov Model","volume":"21","author":"Bae","year":"2011","journal-title":"Mechatronics"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Woodbridge, J., Lan, M., Sarrafzadeh, M., and Bui, A. (2011, January 26\u201329). Salient Segmentation of Medical Time Series Signals. San Jose, CA, USA.","DOI":"10.1109\/HISB.2011.41"},{"key":"ref_82","unstructured":"Al-Hajji, A.A. (2012, January 12\u201314). Rule-Based Expert System for Diagnosis and Symptom of Neurological Disorders Neurologist Expert System (NES). Al-Madinah Al-Munawwarah, Saudi Arabia."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"He, J., Liu, X., Krupinski, E., and Xu, G. (2012). Health Information Science, Springer.","DOI":"10.1007\/978-3-642-29361-0"},{"key":"ref_84","unstructured":"Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R. (1996). Advances in Knowledge Discovery and Data Mining, American Association for Artificial Intelligence."},{"key":"ref_85","unstructured":"Kalagnanam, J., and Henrion, M. (2013). A comparison of decision analysis and expert rules for sequential diagnosis. arXiv:1304.2362."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Pang, C., Mcbride, S., Hansen, D., Cheung, C., and Steyn, M. (2010, January 13\u201315). Towards Health Data Stream Analytics. Gold Coast, Australia.","DOI":"10.1109\/ICCME.2010.5558827"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"6:1","DOI":"10.1145\/1883612.1883613","article-title":"Discrete Wavelet transform-based time series analysis and mining","volume":"43","author":"Chaovalit","year":"2011","journal-title":"ACM Comput. Surv."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1109\/TBME.2011.2163157","article-title":"Physiological parameter monitoring from optical recordings with a mobile phone","volume":"59","author":"Scully","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Salem, O., Liu, Y., and Mehaoua, A. (2013, January 7\u201310). A Lightweight Anomaly Detection Framework for Medical Wireless Sensor Networks. Shanghai, China.","DOI":"10.1109\/WCNC.2013.6555279"},{"key":"ref_90","unstructured":"PhysioBank Archive Index. Available online: http:\/\/www.physionet.org\/physiobank\/database\/."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1161\/01.CIR.101.23.e215","article-title":"PhysioBank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals","volume":"101","author":"Goldberger","year":"2000","journal-title":"Circulation"},{"key":"ref_92","first-page":"40","article-title":"Real time ECG feature extraction and arrhythmia detection on a mobile platform","volume":"44","author":"Patel","year":"2012","journal-title":"Int. J. Comput. Appl."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Yang, S., Kim, J., and Gerla, M. (2012, January 11\u201314). Clinical Quality Guaranteed Physiological Data Compression in Mobile Health Monitoring. Hilton Head, SC, USA.","DOI":"10.1145\/2248341.2248351"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"He, X., Goubran, R.A., and Liu, X.P. (2012, January 18\u201319). Ensemble Empirical Mode Decomposition and Adaptive Filtering for ECG Signal Enhancement. Budapest, Hungary.","DOI":"10.1109\/MeMeA.2012.6226649"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Ramesh, M.V., Anu, T.A., and Thirugnanam, H. (2012, January 20\u201322). An Intelligent Decision Support System for Enhancing an m-Health Application. Indore, India.","DOI":"10.1109\/WOCN.2012.6335564"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1016\/j.eswa.2010.07.136","article-title":"Web-based remote human pulse monitoring system with intelligent data analysis for home health care","volume":"38","author":"Chen","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_97","first-page":"107046","article-title":"Sleep stage classification using unsupervised feature learning","volume":"2012","author":"Karlsson","year":"2012","journal-title":"Adv. Artif. Neu. Sys."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Kim, J., Kim, J., Lee, D., and Chung, K.Y. (2012). Ontology driven interactive healthcare with wearable sensors. Multimed. Tools Appl.","DOI":"10.1007\/s11042-012-1195-9"},{"key":"ref_99","unstructured":"Alirezaie, M., and Loutfi, A. (2013, January 19\u201322). Automatic Annotation of Sensor Data Streams Using Abductive Reasoning. Vilamoura, Portugal."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"380239","DOI":"10.1155\/2013\/380239","article-title":"Health monitoring for elderly: An application using case-based reasoning and cluster analysis","volume":"2013","author":"Ahmed","year":"2013","journal-title":"ISRN Artif. Intell."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Banaee, H., Ahmed, M.U., and Loutfi, A. (2013, January 13\u201316). A Framework for Automatic Text Generation of Trends in Physiological Time Series Data. Manchester, UK.","DOI":"10.1109\/SMC.2013.661"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.artmed.2012.09.002","article-title":"Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse","volume":"56","author":"Hunter","year":"2012","journal-title":"Artif. Intell. Med."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/12\/17472\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:51:26Z","timestamp":1760219486000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/13\/12\/17472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,12,17]]},"references-count":102,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2013,12]]}},"alternative-id":["s131217472"],"URL":"https:\/\/doi.org\/10.3390\/s131217472","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2013,12,17]]}}}