Data mining for wearable sensors in health monitoring systems: A review of recent trends and challenges

Sensors (Switzerland)

Volumn 13, Issue 12, 2013, Pages 17472-17500

  Banaee, Hadi ^a   Ahmed, Mobyen Uddin ^a   Loutfi, Amy ^a  

^a ÖREBRO UNIVERSITY   (Sweden)

Author keywords

Data mining; Health monitoring system; Healthcare; Machine learning technique; Medical informatics; Physiological sensors; Vital signs; Wearable sensors

Indexed keywords

DATA MINING; HEALTH CARE; LEARNING SYSTEMS; PATIENT MONITORING; PHYSIOLOGY;

HEALTH MONITORING SYSTEM; MACHINE LEARNING TECHNIQUES; MEDICAL INFORMATICS; PHYSIOLOGICAL SENSORS; VITAL SIGN; WEARABLE SENSORS;

SENSORS;

EID: 84890453428     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s131217472     Document Type: Review

References (102)

1. Sow, D.; Turaga, D.; Schmidt, M. Mining of Sensor Data in Healthcare: A Survey. In Managing and Mining Sensor Data; Aggarwal, C.C., Ed.; Springer: Berlin, Germany, 2013; pp. 459-504
2. Suh, M.K.; Chen, C.A.; Woodbridge, J.; Tu, M.; Kim, J.; Nahapetian, A.; Evangelista, L.; Sarrafzadeh, M. A remote patient monitoring system for congestive heart failure. J. Med. Syst. 2011, 35, 1165-1179
3. Youm, S.; Lee, G.; Park, S.; Zhu, W. Development of remote healthcare system for measuring and promoting healthy lifestyle. Expert Syst. Appl. 2011, 38, 2828-2834
4. Malhi, K.; Mukhopadhyay, S.C.; Schnepper, J.; Haefke, M.; Ewald, H. A Zigbee-based wearable physiological parameters monitoring system. IEEE Sens. J. 2012, 12, 423-430
5. Yamada, I.; Lopez, G. Wearable Sensing Systems for Healthcare Monitoring. In Proceedings of the Symposium on VLSI Technology, Honolulu, HI, USA, 12-14 June 2012; pp. 5-10
6. Chen, M.; Gonzalez, S.; Vasilakos, A.; Cao, H.; Leung, V.C. Body area networks: A survey. Mob. Netw. Appl. 2011, 16, 171-193
7. Custodio, V.; Herrera, F.J.; López, G.; Moreno, J.I. A review on architectures and communications technologies for wearable health-monitoring systems. Sensors 2012, 12, 13907-13946
8. Pantelopoulos, A.; Bourbakis, N.G. A survey on wearable sensor-based systems for health monitoring and prognosis. Trans. Syst. Man Cyber. Part C 2010, 40, 1-12
9. Alemdar, H.; Ersoy, C. Wireless sensor networks for healthcare: A survey. Comput. Netw. 2010, 54, 2688-2710
10. Baig, M.; Gholamhosseini, H. Smart health monitoring systems: An overview of design and modeling. J. Med. Syst. 2013, 37, 1-14
11. Rashidi, P.; Mihailidis, A. A survey on ambient-assisted living tools for older adults. IEEE J. Biomed. Health Inform. 2013, 17, 579-590
12. Atallah, L.; Lo, B.; Yang, G.Z. Can pervasive sensing address current challenges in global healthcare? J. Epidemiol. Glob. Health 2012, 2, 1-13
13. Lara, O.D.; Labrador, M.A. A survey on ambient-assisted living tools for older adults. IEEE Commun. Surv. Tutor. 2013, 15, 1192-1209
14. Avci, A.; Bosch, S.; Marin-Perianu, M.; Marin-Perianu, R.; Havinga, P. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey. In Proceedings of the 23th International Conference on Architecture of Computing Systems, Hannover, Germany, 22-23 February 2010; pp. 167-176
15. Mannini, A.; Sabatini, A.M. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 2010, 10, 1154-1175
16. Patel, S.; Park, H.; Bonato, P.; Chan, L.; Rodgers, M. A review of wearable sensors and systems with application in rehabilitation. J. Neuroeng. Rehabil. 2012, 9, 1-17
17. Chan, M.; Estéve, D.; Fourniols, J.Y.; Escriba, C.; Campo, E. Smart wearable systems: Current status and future challenges. Artif. Intell. Med. 2012, 56, 137-156
18. Bellazzi, R.; Ferrazzi, F.; Sacchi, L. Predictive data mining in clinical medicine: A focus on selected methods and applications. Wiley. Interdiscip. Rev.: Data. Min. Knowl. Discov. 2011, 1, 416-430
19. Nangalia, V.; Prytherch, D.; Smith, G. Health technology assessment review: Remote monitoring of vital signs-current status and future challenges. Crit. Care 2010, 14, 1-8
20. Yoo, I.; Alafaireet, P.; Marinov, M.; Pena-Hernandez, K.; Gopidi, R.; Chang, J.F.; Hua, L. Data mining in healthcare and biomedicine: A survey of the literature. J. Med. Syst. 2012, 36, 2431-2448
21. Stacey, M.; McGregor, C. Temporal abstraction in intelligent clinical data analysis: A survey. Artif. Intell. Med. 2007, 39, 1-24
22. Mukherjee, A.; Pal, A.; Misra, P. Data Analytics in Ubiquitous Sensor-Based Health Information Systems. In Proceedings of the 2012 6th International Conference on Next Generation Mobile Applications, Services and Technologies, Paris, France, 12-14 September 2012; pp. 193-198
23. Chatterjee, S.; Dutta, K.; Xie, H.Q.; Byun, J.; Pottathil, A.; Moore, M. Persuasive and Pervasive Sensing: A New Frontier to Monitor, Track and Assist Older Adults Suffering from Type-2 Diabetes. In Proceedings of the 46th Hawaii International Conference on System Sciences, Grand Wailea, Maui, HI, USA, 7-10 January 2013; pp. 2636-2645
24. Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey. ACM Comput. Surv. 2009, 41, 151-1558
25. Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection for discrete sequences: A survey. IEEE Trans. Knowl. Data Eng. 2012, 24, 823-839
26. Gaura, E.; Kemp, J.; Brusey, J. Leveraging knowledge from physiological data: On-body heat stress risk prediction with sensor networks. IEEE Trans. Biomed. Circuits Syst. 2013, in press.
27. Lee, K.H.; Kung, S.Y.; Verma, N. Low-energy formulations of support vector machine kernel functions for biomedical sensor applications. J. Signal Process. Syst. 2012, 69, 339-349
28. Zhu, Y. Automatic detection of anomalies in blood glucose using a machine learning approach. J. Commun. Netw. 2011, 13, 125-131
29. Gialelis, J.; Chondros, P.; Karadimas, D.; Dima, S.; Serpanos, D. Identifying Chronic Disease Complications Utilizing State of the Art Data Fusion Methodologies and Signal Processing Algorithms. In Wireless Mobile Communication and Healthcare; Nikita, K.S., Lin, J.C., Fotiadis, D.I., Arredondo Waldmeyer, M.T., Eds.; Springer: Berlin, Germany, 2012; Volume 83, pp. 256-263
30. Huang, G.; Zhang, Y.; Cao, J.; Steyn, M.; Taraporewalla, K. Online mining abnormal period patterns from multiple medical sensor data streams. World Wide Web 2013, doi:10.1007/s11280-013-0203-y.
31. Clifton, L.; Clifton, D.A.; Pimentel, M.A.F.; Watkinson, P.J.; Tarassenko, L. Gaussian processes for personalized e-health monitoring with wearable sensors. IEEE Trans. Biomed. Eng. 2013, 60, 193-197
32. Thakker, B.; Vyas, A.L. Support vector machine for abnormal pulse classification. Int. J. Comput. Appl. 2011, 22, 13-19
33. Charbonnier, S.; Gentil, S. On-line adaptive trend extraction of multiple physiological signals for alarm filtering in intensive care units. Int. J. Adapt. Control. Signal. Process. 2009, 24, 382-408
34. Adnane, M.; Jiang, Z.; Choi, S.; Jang, H. Detecting specific health-related events using an integrated sensor system for vital sign monitoring. Sensors 2009, 9, 6897-6912
35. Adnane, M.; Jiang, Z.; Mori, N.; Matsumoto, Y. An Automated Program for Mental Stress and Apnea/Hypopnea Events Detection. In Proceedings of the 7th International Workshop on Systems, Signal Processing and their Applications, Tipaza, Algeria, 9-11 May 2011; pp. 59-62
36. Singh, R.R.; Conjeti, S.; Banerjee, R. An Approach for Real-Time Stress-Trend Detection Using Physiological Signals in Wearable Computing Systems for Automotive Drivers. In Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA, 5-7 October 2011; pp. 1477-1482
37. Bellazzi, R.; Zupan, B. Predictive data mining in clinical medicine: Current issues and guidelines. Int. J. Med. Inform. 2008, 77, 81-97
38. Silva, F.; Olivares, T.; Royo, F.; Vergara, M.A.; Analide, C. Experimental Study of the Stress Level at the Workplace Using an Smart Testbed of Wireless Sensor Networks and Ambient Intelligence Techniques. In Natural and Artificial Computation in Engineering and Medical Applications; Ferrández Vicente, J., Á lvarez Sánchez, J., Paz López, F., Toledo Moreo, F.J., Eds.; Springer: Berlin, Germany, 2013; Volume 7931, pp. 200-209
39. Sun, F.T.; Kuo, C.; Cheng, H.T.; Buthpitiya, S.; Collins, P.; Griss, M. Activity-Aware Mental Stress Detection Using Physiological Sensors. In Mobile Computing, Applications, and Services; Gris, M., Yang, G., Eds.; Springer: Berlin, Germany, 2012; Volume 76, pp. 211-230
40. Marlin, B.M.; Kale, D.C.; Khemani, R.G.; Wetzel, R.C. Unsupervised Pattern Discovery in Electronic Health Care Data Using Probabilistic Clustering Models. In Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium, Miami, FL, USA, January 2012; pp. 389-398
41. Yeh, J.Y.; Wu, T.H.; Tsao, C.W. Using data mining techniques to predict hospitalization of hemodialysis patients. Decis. Support Syst. 2011, 50, 439-448
42. Choi, J.; Ahmed, B.; Gutierrez-Osuna, R. Development and evaluation of an ambulatory stress monitor based on wearable sensors. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 279-286
43. Sneha, S.; Varshney, U. Enabling ubiquitous patient monitoring: Model, decision protocols, opportunities and challenges. Decis. Support Syst. 2009, 46, 606-619
44. Bellos, C.; Papadopoulos, A.; Rosso, R.; Fotiadis, D.I. A Support Vector Machine Approach for Categorization of Patients Suffering from Chronic Diseases. In Wireless Mobile Communication and Healthcare; Nikita, K.S., Lin, J.C., Fotiadis, D.I., Arredondo Waldmeyer, M.T., Eds.; Springer: Berlin, Germany, 2012; Volume 83, pp. 264-267
45. Giri, D.; Rajendra Acharya, U.; Martis, R.J.; Vinitha Sree, S.; Lim, T.C.; Ahamed VI T.; Suri, J.S. Automated diagnosis of Coronary Artery Disease affected patients using LDA, PCA, ICA and Discrete Wavelet Transform. Know. Based Syst. 2013, 37, 274-282
46. Bellos, C.; Papadopoulos, A.; Rosso, R.; Fotiadis, D.I. Categorization of Patients' Health Status in Copd Disease Using a Wearable Platform and Random Forests Methodology. In Proceedings of the IEEE International Conference on Biomedical and Health Informatics, Shenzhen, China, 5-7 January 2012; pp. 404-407
47. Bianchi, A.M.; Mendez, M.O.; Cerutti, S. Processing of signals recorded through smart devices: Sleep-quality assessment. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 741-747
48. Xie, B.; Minn, H. Real-time sleep apnea detection by classifier combination. IEEE Trans. Inf. Technol. Biomed. 2012, 16, 469-477
49. Vu, T.H.N.; Park, N.; Lee, Y.K.; Lee, Y.; Lee, J.Y.; Ryu, K.H. Online discovery of Heart Rate Variability patterns in mobile healthcare services. J. Syst. Softw. 2010, 83, 1930-1940
50. Pantelopoulos, A.; Bourbakis, N.G. Prognosis-a wearable health-monitoring system for people at risk: Methodology and modeling. IEEE Trans. Inf. Technol. Biomed. 2010, 14, 613-621
51. Frantzidis, C.A.; Bratsas, C.; Klados, M.A.; Konstantinidis, E.; Lithari, C.D.; Vivas, A.B.; Papadelis, C.L.; Kaldoudi, E.; Pappas, C.; Bamidis, P.D. On the classification of emotional biosignals evoked while viewing affective pictures: An integrated data-mining-based approach for healthcare applications. Trans. Inf. Tech. Biomed. 2010, 14, 309-318
52. Karlen, W.; Mattiussi, C.; Floreano, D. Sleep and wake classification with ECG and respiratory effort signals. IEEE Trans. Biomed. Circuits Syst. 2009, 3, 71-78
53. Naraharisetti, K.V.P.; Bawa, M.; Tahernezhadi, M. Comparison of Different Signal Processing Methods for Reducing Artifacts from Photoplethysmograph Signal. In Proceedings of the IEEE International Conference on Electro/Information Technology, Mankato, MN, USA, 15-17 May 2011; pp. 1-8
54. Ding, H.; Sun, H.; mean Hou, K. Abnormal ECG Signal Detection Based on Compressed Sampling in Wearable ECG Sensor. In Proceedings of the International Conference on Wireless Communications and Signal Processing, Nanjing, China, 9-11 November 2011; pp. 1-5
55. Yoon, J. Three-Tiered Data Mining for Big Data Patterns ofWireless Sensor Networks in Medical and Healthcare Domains. In Proceedings of the 8th International Conference on Internet andWeb Applications and Services, Rome, Italy, 23-28 June 2013; pp. 18-24
56. Ahmad, N.F.; Hoang, D.B.; Phung, M.H. Robust Preprocessing for Health Care Monitoring Framework. In Proceedings of the 11th International Conference on E-Health Networking, Applications and Services, Sydney, Australia, 16-18 December 2009; pp. 169-174
57. Wang, W.; Wang, H.; Hempel, M.; Peng, D.; Sharif, H.; Chen, H.H. Secure stochastic ECG signals based on gaussian mixture model for e-healthcare systems. IEEE Syst. J. 2011, 5, 564-573
58. Hjalmarson, A. Heart rate: An independent risk factor in cardiovascular disease. Eur. Heart J. Suppl. 2007, 9, F3-F7
59. Gellish, R.; Goslin, B.; Olson, R.; McDonald, A.; Russi, G.; Moudgil, V. Longitudinal modeling of the relationship between age and maximal heart rate. Med. Sci. Sports Exerc. 2007, 39, 822-829
60. Mao, Y.; Chen, W.; Chen, Y.; Lu, C.; Kollef, M.; Bailey, T. An Integrated Data Mining Approach to Real-Time Clinical Monitoring and Deterioration Warning. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, Beijing, China, 16-18 August 2012; pp. 1140-1148
61. Apiletti, D.; Baralis, E.; Bruno, G.; Cerquitelli, T. Real-time analysis of physiological data to support medical applications. Trans. Info. Tech. Biomed. 2009, 13, 313-321
62. Hu, F.; Jiang, M.; Celentano, L.; Xiao, Y. Robust medical ad hoc sensor networks (MASN) with wavelet-based ECG data mining. Ad Hoc Netw. 2008, 6, 986-1012
63. Guyon, I.; Gunn, S.; Nikravesh, M.; Zadeh, L.A. Feature Extraction: Foundations and Applications (Studies in Fuzziness and Soft Computing); Springer: Secaucus, NJ, USA, 2006
64. Bellos, C.C.; Papadopoulos, A.; Rosso, R.; Fotiadis, D.I. Extraction and Analysis of Features Acquired By Wearable Sensors Network. In Proceedings of 10th IEEE International Conference on Information Technology and Applications in Biomedicine, Corfu, Greece, 3-5 November 2010; pp. 1-4
65. Bsoul, M.; Minn, H.; Tamil, L. Apnea medassist: Real-time sleep apnea monitor using single-lead ECG. IEEE Trans. Inf. Technol. Biomed. 2011, 15, 416-427
66. Widodo, A.; Yang, B.S. Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors. Expert Syst. Appl. 2007, 33, 241-250
67. Li, X.; Porikli, F. Human State Classification and Predication for Critical Care Monitoring by Real-Time Bio-signal Analysis. In Proceedings of the 20th International Conference on Pattern Recognition, Istanbul, Turkey, 23-26 August 2010; pp. 2460-2463
68. Pradhan, G.N.; Chattopadhyay, R.; Panchanathan, S. Processing Body Sensor Data Streams for Continuous Physiological Monitoring. In Proceedings of the International Conference on Multimedia Information Retrieval, Philadelphia, PA, USA, 29-31 March 2010; pp. 479-486
69. Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn. 1995, 20, 273-297
70. Paliwal, M.; Kumar, U.A. Neural networks and statistical techniques: A review of applications. Expert. Syst. Appl. 2009, 36, 2-17
71. Amato, F.; López Rodríguez, A.; Peñaand Méndez, E.M.; Vaňhara, P.; Hampl, A.; Havel, J. Artificial neural networks in medical diagnosis. J Appl. Biomed. 2013, 11, 47-58
72. Li, Q.; Clifford, G.D. Dynamic time warping and machine learning for signal quality assessment of pulsatile signals. Physiol. Meas. 2012, 33, 1491-1501
73. Blonde, L.; Karter, A.J. Current evidence regarding the value of self-monitored blood glucose testing. Am. J. Med. 2005, 118, 20-26
74. Jin, Z.; Sun, Y.; Cheng, A.C. Predicting Cardiovascular Disease From Real-Time Electrocardiographic Monitoring: An Adaptive Machine Learning Approach on a Cell Phone. In Proceedings of the 31th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, 3-6 September 2009; pp. 6889-6892
75. Ordonez, P.; Armstrong, T.; Oates, T.; Fackler, J. Classification of Patients Using Novel Multivariate Time Series Representations of Physiological Data. In Proceedings of the 10th International Conference on Machine Learning and Applications, Honolulu, HI, USA, 18-21 December 2011; pp. 172-179
76. Podgorelec, V.; Kokol, P.; Stiglic, B.; Rozman, I. Decision trees: An overview and their use in medicine. J. Med. Syst. 2002, 26, 445-463
77. López-Vallverdú, J.A.; Riaño, D.; Bohada, J.A. Improving medical decision trees by combining relevant health-care criteria. Expert Syst. Appl. 2012, 39, 11782-11791
78. Rabiner, L.; Juang, B.H. An introduction to hidden Markov models. IEEE ASSP Mag. 1986, 3, 4-16
79. Thomas, O.; Sunehag, P.; Dror, G.; Yun, S.; Kim, S.; Robards, M.; Smola, A.; Green, D.; Saunders, P. Wearable sensor activity analysis using semi-Markov models with a grammar. Pervasive Mob. Comput. 2010, 6, 342-350
80. Bae, J.; Tomizuka, M. Gait phase analysis based on a Hidden Markov Model. Mechatronics 2011, 21, 961-970
81. Woodbridge, J.; Lan, M.; Sarrafzadeh, M.; Bui, A. Salient Segmentation of Medical Time Series Signals. In Proceedings of the First IEEE International Conference on Healthcare Informatics, Imaging and Systems Biology, San Jose, CA, USA, 26-29 July 2011; pp. 1-8
82. Al-Hajji, A.A. Rule-Based Expert System for Diagnosis and Symptom of Neurological Disorders "Neurologist Expert System (NES)". In Proceedings of the 1st Taibah University International Conference on Computing and Information Technology, Al-Madinah Al-Munawwarah, Saudi Arabia, 12-14 March 2012; pp. 67-72
83. He, J.; Zhang, Y.; Huang, G.; Xin, Y.; Liu, X.; Zhang, H.; Chiang, S.; Zhang, H. An Association Rule Analysis Framework for Complex Physiological and Genetic Data. In Health Information Science; He, J., Liu, X., Krupinski, E., Xu, G., Eds.; Springer: Berlin, Germany, 2012; pp. 131-142
84. Fayyad, U.M.; Piatetsky-Shapiro, G.; Smyth, P. From Data Mining to Knowledge Discovery: An Overview. In Advances in Knowledge Discovery and Data Mining; Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R., Eds.; American Association for Artificial Intelligence: Menlo Park, CA, USA, 1996; pp. 1-34
85. Kalagnanam, J.; Henrion, M. A comparison of decision analysis and expert rules for sequential diagnosis. 2013, arXiv:1304.2362
86. Zhang, Q.; Pang, C.; Mcbride, S.; Hansen, D.; Cheung, C.; Steyn, M. Towards Health Data Stream Analytics. In Proceedings of the IEEE/ICME International Conference on Complex Medical Engineering, Gold Coast, Australia, 13-15 July 2010; pp. 282-287
87. Chaovalit, P.; Gangopadhyay, A.; Karabatis, G.; Chen, Z. DiscreteWavelet transform-based time series analysis and mining. ACM Comput. Surv. 2011, 43, 61-637
88. Scully, C.; Lee, J.; Meyer, J.; Gorbach, A.M.; Granquist-Fraser, D.; Mendelson, Y.; Chon, K.H. Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans. Biomed. Eng. 2012, 59, 303-306
89. Salem, O.; Liu, Y.; Mehaoua, A. A Lightweight Anomaly Detection Framework for Medical Wireless Sensor Networks. In Proceedings of the IEEE Wireless Communications and Networking Conference, Shanghai, China, 7-10 April 2013; pp. 4358-4363
90. PhysioBank Archive Index. Available online: http://www.physionet.org/physiobank/database/ (accessed on 10 November 2013).
91. Goldberger, A.L.; Amaral, L.A.N.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E. PhysioBank, physiotoolkit, and physionet: Components of a new research resource for complex physiologic signals. Circulation 2000, 101, 215-220
92. Patel, A.M.; Gakare, P.K.; Cheeran, A.N. Real time ECG feature extraction and arrhythmia detection on a mobile platform. Int. J. Comput. Appl. 2012, 44, 40-45
93. Yang, S.; Kim, J.; Gerla, M. Clinical Quality Guaranteed Physiological Data Compression in Mobile Health Monitoring. In Proceedings of the 2nd ACM InternationalWorkshop on Pervasive Wireless Healthcare, Hilton Head, SC, USA, 11-14 June 2012; pp. 51-56
94. He, X.; Goubran, R.A.; Liu, X.P. Ensemble Empirical Mode Decomposition and Adaptive Filtering for ECG Signal Enhancement. In Proceedings of the IEEE International Symposium on Medical Measurements and Applications Proceedings, Budapest, Hungary, 18-19 May 2012; pp. 1-5
95. Ramesh, M.V.; Anu, T.A.; Thirugnanam, H. An Intelligent Decision Support System for Enhancing an m-Health Application. In Proceedings of the 9th International Conference on Wireless and Optical Communications Networks, Indore, India, 20-22 September 2012; pp. 1-5
96. Chen, C.M. Web-based remote human pulse monitoring system with intelligent data analysis for home health care. Expert Syst. Appl. 2011, 38, 2011-2019
97. Längkvist, M.; Karlsson, L.; Loutfi, A. Sleep stage classification using unsupervised feature learning. Adv. Artif. Neu. Sys. 2012, 2012, 107046
98. Kim, J.; Kim, J.; Lee, D.; Chung, K.Y. Ontology driven interactive healthcare with wearable sensors. Multimed. Tools Appl. 2012, doi:10.1007/s11042-012-1195-9
99. Alirezaie, M.; Loutfi, A. Automatic Annotation of Sensor Data Streams Using Abductive Reasoning. Presented at the 5th International Conference on Knowledge Engineering and Ontology Development, Vilamoura, Portugal, 19-22 September 2013
100. Ahmed, M.U.; Banaee, H.; Loutfi, A. Health monitoring for elderly: An application using case-based reasoning and cluster analysis. ISRN Artif. Intell. 2013, 2013, 380239
101. Banaee, H.; Ahmed, M.U.; Loutfi, A. A Framework for Automatic Text Generation of Trends in Physiological Time Series Data. In Proceedings of the IEEE International Conference on System, Man, and Cybernetics, Manchester, UK, 13-16 October 2013; pp. 3876-3881
102. Hunter, J.; Freer, Y.; Gatt, A.; Reiter, E.; Sripada, S.; Sykes, C. Automatic generation of natural language nursing shift summaries in neonatal intensive care: BT-Nurse. Artif. Intell. Med. 2012, 56, 157-172.