An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments - Publikacja - MOST Wiedzy


An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments


The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.


  • 1 1 4


  • 7 9

    Web of Science

  • 1 1 4


Autorzy (5)

Cytuj jako

Pełna treść

pobierz publikację
pobrano 90 razy
Wersja publikacji
Accepted albo Published Version
Creative Commons: CC-BY otwiera się w nowej karcie

Słowa kluczowe

Informacje szczegółowe

Publikacja w czasopiśmie
artykuł w czasopiśmie wyróżnionym w JCR
Opublikowano w:
SENSORS nr 17, wydanie 10, strony 1 - 25,
ISSN: 1424-8220
Rok wydania:
Opis bibliograficzny:
Mora H., Gil D., Munoz Terol R., Azorin-Lopez J., Szymański J.: An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments// SENSORS-BASEL. -Vol. 17, iss. 10 (2017), s.1-25
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/s17102302
Bibliografia: test
  1. García, M. The Impact of IoT on Economic Growth: A Multifactor Productivity Approach. In Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 7-9 December 2015. otwiera się w nowej karcie
  2. Visvizi, A.; Mazzucelli, C.; Lytras, M. Irregular migratory flows: Towards an ICTs' enabled integrated framework for resilient urban systems. J. Sci. Technol. Policy Manag. 2017, 8, 227-242. [CrossRef] otwiera się w nowej karcie
  3. Silva, B.M.; Rodrigues, J.J.; de la Torre Díez, I.; López-Coronado, M.; Saleem, K. Mobile-health: A review of current state in 2015. J. Biomed. Inform. 2015, 56, 265-272. [CrossRef] [PubMed] otwiera się w nowej karcie
  4. Gil, D.; Ferrández, A.; Mora-Mora, H.; Peral, J. Internet of Things: A Review of Surveys Based on Context Aware Intelligent Services. Sensors 2016, 16, 1069. [CrossRef] [PubMed] otwiera się w nowej karcie
  5. Colom, J.F.; Mora, H.; Gil, D.; Signes-Pont, M.T. Collaborative building of behavioural models based on internet of things. Comput. Electr. Eng. 2016, 58, 385-396. [CrossRef] otwiera się w nowej karcie
  6. Lanza Calderón, J.; Sotres García, P.; Sánchez González, L.; Galache López, J.A.; Santana Martínez, J.R.; Gutiérrez Polidura, V.; Muñoz Gutiérrez, L. Managing Large Amounts of Data Generated by a Smart City Internet of Things Deployment. Int. J. Semant. Web Inf. Syst. 2016, 12. [CrossRef] otwiera się w nowej karcie
  7. Llaves, A.; Corcho, O.; Taylor, P.; Taylor, K. Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain. Int. J. Semant. Web Inf. Syst. 2016, 12. [CrossRef] otwiera się w nowej karcie
  8. Vujović, V.; Maksimović, M. Raspberry Pi as a Sensor Web node for home automation. Comput. Electr. Eng. 2015, 44, 153-171. [CrossRef] otwiera się w nowej karcie
  9. Gilart-Iglesias, V.; Mora, H.; Pérez-delHoyo, R.; García-Mayor, C. A computational method based on radio frequency technologies for the analysis of accessibility of disabled people in sustainable cities. Sustainability 2015, 7, 14935-14963. [CrossRef] otwiera się w nowej karcie
  10. Ferrández-Pastor, F.J.; Mora-Mora, H.; Sánchez-Romero, J.L.; Nieto-Hidalgo, M.; García-Chamizo, J.M. Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models. J. Ambient Intell. Hum. Comput. 2017, 8, 469-483. [CrossRef] otwiera się w nowej karcie
  11. Laplante, P.A.; Laplante, N. The Internet of Things in Healthcare: Potential Applications and Challenges. IT Prof. 2016, 18, 2-4. [CrossRef] otwiera się w nowej karcie
  12. Chen, R.C.; Hsieh, C.F.; Chang, W. Using ambient intelligence to extend network lifetime in wireless sensor networks. J. Ambient Intell. Hum. Comput. 2016, 7, 777-788. [CrossRef] otwiera się w nowej karcie
  13. Santos, J.; Rodrigues, J.J.; Silva, B.M.; Casal, J.; Saleem, K.; Denisov, V. An IoT-based mobile gateway for intelligent personal assistants on mobile health environments. J. Netw. Comput. Appl. 2016, 71, 194-204. [CrossRef] otwiera się w nowej karcie
  14. Kalem, G.; Turhan, Ç. Mobile Technology Applications in the Healthcare Industry for Disease Management and Wellness. Procedia Soc. Behav. Sci. 2015, 195, 2014-2018. [CrossRef] otwiera się w nowej karcie
  15. Krishnamoorthy, S. Nanostructured sensors for biomedical applications-A current perspective. Curr. Opin. Biotechnol. 2015, 34, 118-124. [CrossRef] [PubMed] otwiera się w nowej karcie
  16. Ma, C.Z.-H.; Wong, D.W.-C.; Lam, W.K.; Wan, A.H.-P.; Lee, W.C.-C. Balance Improvement Effects of Biofeedback Systems with State-of-the-Art Wearable Sensors: A Systematic Review. Sensors 2016, 16, 434. [CrossRef] [PubMed] otwiera się w nowej karcie
  17. Mandl, K.D.; Mandel, J.C.; Kohane, I.S. Driving Innovation in Health Systems through an Apps-Based Information Economy. Cell Syst. 2015, 1, 8-13. [CrossRef] [PubMed] otwiera się w nowej karcie
  18. Baldwin, J.L.; Singh, H.; Sittig, D.F.; Giardina, T.D. Patient portals and health apps: Pitfalls, promises, and what one might learn from the other. Healthcare 2016. [CrossRef] [PubMed] otwiera się w nowej karcie
  19. Fafoutis, X.; Janko, B.; Mellios, E.; Hilton, G.; Sherratt, S.; Piechocki, R.; Craddock, I. SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications. In Proceedings of the EAI International Conference on Wearables in Healthcare, Budapest, Hungary, 14-15 June 2016. otwiera się w nowej karcie
  20. Mashal, I.; Alsaryrah, O.; Chung, T.Y. Testing and evaluating recommendation algorithms in internet of things. J. Ambient Intell. Hum. Comput. 2016, 7, 889-900. [CrossRef] otwiera się w nowej karcie
  21. Wu, Q.; Ding, G.; Xu, Y.; Feng, S.; Du, Z.; Wang, J.; Long, K. Cognitive Internet of Things: A New Paradigm Beyond Connection. IEEE Internet Things J. 2014, 1, 129-143. [CrossRef] otwiera się w nowej karcie
  22. Feng, S.; Setoodeh, P.; Haykin, S. Smart Home: Cognitive Interactive People-Centric Internet of Things. Commun. Mag. IEEE 2017, 55, 34-39. [CrossRef] otwiera się w nowej karcie
  23. Finocchiaro, G.; Papadakis, M.; Robertus, J.L.; Dhutia, H.; Steriotis, A.K.; Tome, M.; Sharma, S. Etiology of Sudden Death in Sports: Insights from a United Kingdom Regional Registry. J. Am. Coll. Cardiol. 2016, 67, 18. [CrossRef] [PubMed] otwiera się w nowej karcie
  24. Chatard, J.C.; Mujika, I.; Goiriena, J.J.; Carré, F. Screening young athletes for prevention of sudden cardiac death: Practical recommendations for sports physicians. Scand. J. Med. Sci. Sports 2016, 26, 362-374. [CrossRef] [PubMed] otwiera się w nowej karcie
  25. Mora, H.; Colom, J.F.; Gil, D.; Jimeno-Morenilla, A. Distributed computational model for shared processing on Cyber-Physical System environments. Comput. Commun. 2017, 111, 68-83. [CrossRef] otwiera się w nowej karcie
  26. Teichmann, D.; Kuhn, A.; Leonhardt, S.; Walter, M. The MAIN Shirt: A textile-integrated magnetic induction sensor array. Sensors 2014, 14, 1039-1056. [CrossRef] [PubMed] otwiera się w nowej karcie
  27. Weyer, S.; Weishaupt, F.; Kleeberg, C.; Leonhardt, S.; Teichmann, D. RheoStim: Development of an Adaptive Multi-Sensor to Prevent Venous Stasis. Sensors 2016, 16, 428. [CrossRef] [PubMed] otwiera się w nowej karcie
  28. Muaremi, A.; Seiter, J.; Tröster, G.; Bexheti, A. Monitor and understand pilgrims: Data collection using smartphones and wearable devices. In Proceedings of the 2013 ACM Conference on Pervasive and Ubiquitous Computing, Zurich, Switzerland, 8-12 September 2013; pp. 679-688. otwiera się w nowej karcie
  29. Arsand, E.; Muzny, M.; Bradway, M.; Muzik, J. Performance of the first combined smartwatch and smartphone diabetes diary application study. J. Diabetes 2015, 9, 556-563. [CrossRef] [PubMed] otwiera się w nowej karcie
  30. Terroso, M.; Freitas, R.; Gabriel, J. Active assistance for senior healthcare: A wearable system for fall detection. In Proceedings of the Iberian Conference on Information Systems and Technologies (CISTI), Lisboa, Portugal, 19-22 October 2013.
  31. Yang, Z.; Zhou, Q.; Lei, L.; Zheng, K.; Xiang, W. An IoT-cloud Based Wearable ECG Monitoring System for Smart Healthcare. J. Med. Syst. 2016, 40, 286. [CrossRef] [PubMed] otwiera się w nowej karcie
  32. Banerjee, A.; Gupta, S.K.S. Analysis of Smart Mobile Applications for Healthcare under Dynamic Context Changes. IEEE Trans. Mob. Comput. 2015, 14, 904-919. [CrossRef] otwiera się w nowej karcie
  33. Ogunduyile, O.O.; Olugbara, O.O.; Lall, M. Development of Wearable Systems for Ubiquitous Healthcare Service Provisioning. APCBEE Procedia 2013, 7, 163-168. [CrossRef] otwiera się w nowej karcie
  34. Zhang, F.; Cao, J.; Khan, S.U.; Li, K.; Hwang, K. A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications. Future Gener. Comput. Syst. 2015, 43-44, 149-160. [CrossRef] otwiera się w nowej karcie
  35. Varshney, U. Pervasive healthcare and wireless health monitoring. J. Mob. Netw. Appl. 2007, 12, 113-127. [CrossRef] otwiera się w nowej karcie
  36. Dihn, H.T.; Lee, C.; Niyato, D.; Wang, P. A survey of mobile cloud computing: architecture, applications, and approaches. Wirel. Commun. Mob. Comput. 2013, 13. [CrossRef] otwiera się w nowej karcie
  37. Halson, S.L. Monitoring Training Load to Understand Fatigue in Athletes. Sports Med. 2014, 44, 139-147. [CrossRef] [PubMed] otwiera się w nowej karcie
  38. Taylor, K.L.; Chapman, D.W.; Cronin, J.; Gill, N.D. Fatigue Monitoring in High Performance Sport: A Survey of Current Trends. J. Aust. Strength Cond. 2012, 20, 12-23.
  39. Michahelles, F.; Schiele, B. Sensing and monitoring professional skiers. IEEE Pervasive Comput. 2005, 4, 40-45. [CrossRef] otwiera się w nowej karcie
  40. Ghasemzadeh, H.; Loseu, V.; Guenterberg, E.; Jafari, R. Sport training using body sensor networks: A statistical approach to measure wrist rotation for golf swing. In Proceedings of the Fourth International Conference on Body Area Networks, Los Angeles, CA, USA, 1-3 April 2009. otwiera się w nowej karcie
  41. Kellman, M.; Kallus, K.W. The Recovery-Stress Questionnaires: User Manual;
  42. Pearson Assessment & Information: Frankfurt, Germany, 2016. otwiera się w nowej karcie
  43. Davis, H.; Orzeck, T.; Keelan, P. Psychometric item evaluations of the Recovery-Stress Questionnaire for athletes. Psychol. Sport Exerc. 2007, 8, 917-938. [CrossRef] otwiera się w nowej karcie
  44. Mcnair, D.; Lorr, M.; Dopplemann, L. Profile of Mood States Manual; Educational and Industrial Testing Service: San Diego, CA, USA, 1992.
  45. Coutts, A.J.; Slattery, K.M.; Wallace, L.K. Practical tests for monitoring performance, fatigue and recovery in triathletes. J. Sci. Med. Sport 2007, 10, 372-381. [CrossRef] [PubMed] otwiera się w nowej karcie
  46. Halson, S.L. Sleep in elite athletes and nutritional interventions to enhance sleep. Sports Med. 2014, 44, 13-23. [CrossRef] [PubMed] otwiera się w nowej karcie
  47. Jobson, S.A.; Passfield, L.; Atkinson, G.; Barton, G.; Scarf, P. The Analysis and Utilization of Cycling Training Data. Sports Med. 2009, 39, 833-844. [CrossRef] [PubMed] otwiera się w nowej karcie
  48. Twist, C.; Highton, J. Monitoring Fatigue and Recovery in Rugby League Players. Int. J. Sports Physiol. Perform. 2013, 8. [CrossRef] otwiera się w nowej karcie
  49. Abdelkrim, N.B.; El Fazaa, S.; El Ati, J. Time-motion analysis and physiological data of elite under-19-year-old basketball players during competition. Br. J. Sports Med. 2007, 41, 69-75. [CrossRef] [PubMed] otwiera się w nowej karcie
  50. Dwyer, D.B.; Gabbett, T. Global Positioning System Data Analysis: Velocity Ranges and a New Definition of Sprinting for Field Sport Athletes. J. Strength Cond. Res. 2012, 26, 818-824. [CrossRef] [PubMed] otwiera się w nowej karcie
  51. Sivaraks, H.; Ratanamahatana, C.A. Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery. Comput. Math. Methods Med. 2015, 453214. [CrossRef] [PubMed] otwiera się w nowej karcie
  52. Senapati, M.K.; Senapati, M.; Maka, S. Cardiac Arrhythmia Classification of ECG Signal Using Morphology and Heart Beat Rate. In Proceedings of the International Conference on Advances in Computing and Communications (ICACC), Cochin, India, 27-29 August 2014. otwiera się w nowej karcie
  53. Pyne, D.B.; Martin, D.T. Fatigue-Insights from individual and team sports. Regul. Fatigue Exerc. 2011, 177-186.
  54. Buchheit, M. Monitoring training status with HR measures: Do all roads lead to Rome? Front. Physiol. 2014, 5. [CrossRef] [PubMed] otwiera się w nowej karcie
  55. Freedson, P.S.; Miller, K. Objective monitoring of physical activity using motion sensors and heart rate. Res. Q. Exerc. Sport 2000, 71, 21-29. [CrossRef] [PubMed] otwiera się w nowej karcie
  56. Daanen, H.A.; Lamberts, R.P.; Kallen, V.L.; Jin, A.; Van Meeteren, N.L. A systematic review on heart-rate recovery to monitor changes in training status in athletes. Int. J. Sports Physiol. Perf. 2012, 7, 251-260. [CrossRef] otwiera się w nowej karcie
  57. Corrado, D.; Pelliccia, A.; Heidbuchel, H.; Sharma, S.; Link, M.; Basso, C.; Anastasakis, A. Recommendations for interpretation of 12-lead electrocardiogram in the athlete. Eur. Heart J. 2010, 31, 243-259. [CrossRef] [PubMed] otwiera się w nowej karcie
  58. McLeod, C.J.; Ackerman, M.J.; Nishimura, R.A.; Tajik, A.J.; Gersh, B.J.; Ommen, S.R. Outcome of Patients With Hypertrophic Cardiomyopathy and a Normal Electrocardiogram. J. Am. Coll. Cardiol. 2009, 54, 229-233. [CrossRef] [PubMed] otwiera się w nowej karcie
  59. Namdar, M.; Steffel, J.; Jetzer, S.; Schmied, C.; Hürlimann, D.; Camici, G.G.; Chierchia, G.B. Value of Electrocardiogram in the Differentiation of Hypertensive Heart Disease, Hypertrophic Cardiomyopathy, Aortic Stenosis, Amyloidosis, and Fabry Disease. Am. J. Cardiol. 2012, 109, 587-593. [CrossRef] [PubMed] otwiera się w nowej karcie
  60. Chen, C.-B.; Lin, C.-C.; Chen, J.-C.; Kuo, C.-W.; Weng, Y.-M. Commotio cordis during prolonged cardiac ventricular repolarization due to exercise-induced hypokalemia: A case report. J. Acute Med. 2016, 6, 19-22. [CrossRef] otwiera się w nowej karcie
  61. Fathala, A.; Hassan, W. Coronary artery anomalies: A diagnostic challenge. J. Saudi Heart Assoc. 2011, 23, 37-39. [CrossRef] [PubMed] otwiera się w nowej karcie
  62. Estes, E.H., Jr.; Jackson, K.P. The electrocardiogram in left ventricular hypertrophy: past and future. J. Electrocardiol. 2009, 42, 589-592. [CrossRef] [PubMed] otwiera się w nowej karcie
  63. Maron, B.J.; Shirani, J.; Poliac, L.C.; Mathenge, R. Sudden death in young competitive athletes: Clinical, demographic, and pathological profiles. JAMA 1996, 276, 199-204. [CrossRef] [PubMed] Sensors 2017, 17, 2302 23 of 25 otwiera się w nowej karcie
  64. Arraiz, G.A.; Wigle, D.T.; Mao, Y. Risk assessment of physical activity and physical fitness in the Canada health survey mortality follow-up study. J. Clin. Epidemiol. 1992, 45, 419-428. [CrossRef] otwiera się w nowej karcie
  65. Ferreira, M.; Santos-Silva, P.R.; de Abreu, L.C.; Valenti, V.E.; Crispim, V.; Imaizumi, C.; Filho, C.F.; Murad, N.; Meneghini, A.; Riera, A.R.P.; et al. Sudden cardiac death athletes: a systematic review. Sports Med. Arthrosc. Rehabilita. Ther. Technol. 2010, 2. [CrossRef] [PubMed] otwiera się w nowej karcie
  66. Corrado, D.; Basso, C.; Rizzoli, G.; Schiavon, M.; Thiene, G. Does sports activity enhance the risk of sudden death in adolescents and young adults? J. Am. Coll. Cardiol. 2003, 42, 1959-1963. [CrossRef] [PubMed] otwiera się w nowej karcie
  67. Maron, B.J.; Pelliccia, A. The heart of trained athletes cardiac remodeling and the risks of sports, including sudden death. Circulation 2006, 114, 1633-1644. [CrossRef] [PubMed] otwiera się w nowej karcie
  68. Marijon, E.; Tafflet, M.; Celermajer, D.S.; Dumas, F.; Perier, M.C.; Mustafic, H.; Le Heuzey, J.Y. Sports-related sudden death in the general population. Circulation 2011, 124, 672-681. [CrossRef] [PubMed] otwiera się w nowej karcie
  69. Steinvil, A.; Chundadze, T.; Zeltser, D.; Rogowski, O.; Halkin, A.; Galily, Y.; Viskin, S. Mandatory electrocardiographic screening of athletes to reduce their risk for sudden death: proven fact or wishful thinking? J. Am. Coll. Cardiol. 2011, 57, 1291-1296. [CrossRef] [PubMed] otwiera się w nowej karcie
  70. Maron, B.J.; Haas, T.S.; Murphy, C.J. Incidence and causes of sudden death in US college athletes. J. Am. Coll. Cardiol. 2014, 63, 1636-1643. [CrossRef] [PubMed] otwiera się w nowej karcie
  71. Solberg, E.E.; Gjertsen, F.; Haugstad, E.; Kolsrud, L. Sudden death in sports among young adults in Norway. Eur. J. Cardiovasc. Prev. Rehabilit. 2010, 17, 337-341. [CrossRef] [PubMed] otwiera się w nowej karcie
  72. Ebrahimzadeh, E.; Pooyan, M.; Bijar, A. A Novel Approach to Predict Sudden Cardiac Death (SCD) Using Nonlinear and Time-Frequency Analyses from HRV Signals. PLoS ONE 2014, 9, e81896. [CrossRef] [PubMed] otwiera się w nowej karcie
  73. Murukesan, L.; Murugappan, M.; Iqbal, M. Sudden cardiac death prediction using ECG signal derivative (Heart Rate Variability): A review. In Proceedings of the IEEE 9th International Colloquium on Signal Processing and its Applications (CSPA), Kuala Lumpur, Malaysia, 8-10 March 2013. otwiera się w nowej karcie
  74. Eranti, A.; Aro, A.L.; Kenttä, T.; Holkeri, A.; Tikkanen, J.T.; Junttila, M.J.; Huikuri, H.V. 12-Lead electrocardiogram as a predictor of sudden cardiac death: from epidemiology to clinical practice. Scand. Cardiovasc. J. 2016, 50. [CrossRef] [PubMed] otwiera się w nowej karcie
  75. Effatparvar, M.; Dehghan, M.; Rahmani, A.M. A comprehensive survey of energy-aware routing protocols in wireless body area sensor networks. J. Med. Syst. 2016, 40, 201. [CrossRef] [PubMed] otwiera się w nowej karcie
  76. Salehi, S.A.; Razzaque, M.A.; Tomeo-Reyes, I.; Hussain, N. IEEE 802.15.6 standard in wireless body area networks from a healthcare point of view. In Proceedings of the Asia-Pacific Conference on Communications (APCC), Yogyakarta, Indonesia, 25-27 August 2016. otwiera się w nowej karcie
  77. Ghamari, M.; Janko, B.; Sherratt, R.S.; Harwin, W.; Piechockic, R.; Soltanpur, C. A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments. Sensors 2016, 16, 831. [CrossRef] [PubMed] otwiera się w nowej karcie
  78. Kwak, K.S.; Ullah, S.; Ullah, N. An overview of IEEE 802.15.6 standard. In Proceedings of the International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), Roma, Italy, 7-10 November 2010. otwiera się w nowej karcie
  79. Sadra, S.; Abolhasan, M. On improving the saturation performance of IEEE802.15.6-based MAC protocols in Wireless Body Area Networks. In Proceedings of the International Wireless Communications and Mobile Computing Conference (IWCMC), Valencia, Spain, 26-30 June 2017; pp. 1233-1238. otwiera się w nowej karcie
  80. Ermes, M.; Pärkkä, J.; Mäntyjärvi, J.; Korhonen, I. Detection of Daily Activities and Sports with Wearable Sensors in Controlled and Uncontrolled Conditions'. IEEE Trans. Inf. Technol. Biomed. 2008, 12, 20-26. [CrossRef] [PubMed] otwiera się w nowej karcie
  81. Hong, Y.-J.; Kim, I.-J.; Chul Ahn, S.; Kim, H.-G. Mobile health monitoring system based on activity recognition using accelerometer. Simul. Model. Pract. Theor. 2010, 18, 446-455. [CrossRef] otwiera się w nowej karcie
  82. Khorov, E.; Lyakhov, A.; Krotov, A.; Guschin, A. A survey on IEEE 802.11ah: An enabling networking technology for smart cities. Comput. Commun. 2015, 58, 53-69. [CrossRef] otwiera się w nowej karcie
  83. Baños-Gonzalez, V.; Afaqui, M.S.; Lopez-Aguilera, E.; Garcia-Villegas, E. IEEE 802.11ah: A Technology to Face the IoT Challenge. Sensors 2016, 16, 1960. [CrossRef] otwiera się w nowej karcie
  84. Yoo, J.; Yoo, H.-J. Emerging low energy Wearable Body Sensor Networks using patch sensors for continuous healthcare applications. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2010. [CrossRef] otwiera się w nowej karcie
  85. Altaf, M.A.B.; Zhang, C.; Radakovic, L.; Yoo, J. Design of energy-efficient on-chip EEG classification and recording processors for wearable environments. In Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada, 22-25 May 2016. otwiera się w nowej karcie
  86. Gao, W.; Emaminejad, S.; Nyein, H.Y.Y.; Challa, S.; Chen, K.; Peck, A.; Fahad, H.M.; Ota, H.; Shiraki, H.; Kiriya, D.; et al. Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis. Nature 2016, 529, 509-514. [CrossRef] [PubMed] otwiera się w nowej karcie
  87. Kornreich, F.; Rautaharju, P.M.; Warren, J.; Montague, T.J.; Horacek, B.M. Identification of best electrocardiographic leads for diagnosing myocardial infarction by statistical analysis of body surface potential maps. Am. J. Cardiol. 1985, 56, 852. [CrossRef] otwiera się w nowej karcie
  88. Green, M.; Ohlsson, M.; Forberg, J.L.; Björk, J.; Edenbrandt, L.; Ekelund, U. Best leads in the standard electrocardiogram for the emergency detection of acute coronary syndrome. J. Electrocardiol. 2007, 40, 251-256. [CrossRef] [PubMed] otwiera się w nowej karcie
  89. Cuomo, S.; De Pietro, G.; Farina, R.; Galletti, A.; Sannino, G. A Novel O(n) Numerical Scheme for ECG Signal Denoising, Procedia Computer Science. Procedia Comput. Sci. 2015, 51, 775-784. [CrossRef] otwiera się w nowej karcie
  90. Luo, S.; Johnston, P. A review of electrocardiogram filtering. J. Electrocardiol. 2010, 43, 486-496. [CrossRef] [PubMed] otwiera się w nowej karcie
  91. Castells-Rufas, D.; Carrabina, J. Simple real-time QRS detector with the MaMeMi filter. Biomed. Signal Process. Control 2015, 21, 137-145. [CrossRef] otwiera się w nowej karcie
  92. Xia, Y.; Han, J.; Wang, K. Quick detection of QRS complexes and R-waves using a wavelet transform and K-means clustering. Biomed. Mater. Eng. 2015, 26. [CrossRef] otwiera się w nowej karcie
  93. Hamdi, S.; Abdallah, A.B.; Bedoui, M.H. Real time QRS complex detection using DFA and regular grammar. Biomed. Eng. Online 2017, 16, 31. [CrossRef] [PubMed] otwiera się w nowej karcie
  94. Kim, J.; Shin, H. Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex. PLoS ONE 2016, 11, e0150144. [CrossRef] [PubMed] otwiera się w nowej karcie
  95. Masud, M.M.; Serhani, M.A.; Navaz, A.N. Resource-Aware Mobile-Based Health Monitoring. IEEE J. Biomed. Health Inform. 2017, 21, 349-360. [CrossRef] [PubMed] otwiera się w nowej karcie
  96. Sahoo, S.; Biswal, P.; Das, T.; Sabut, S. De-noising of ECG Signal and QRS Detection Using Hilbert Transform and Adaptive Thresholding. Procedia Technol. 2016, 25, 68-75. [CrossRef] otwiera się w nowej karcie
  97. Luz, E.J.; Schwartz, W.R.; Cámara-Chávez, G.; Menotti, D. ECG-based heartbeat classification for arrhythmia detection: A survey. Comput. Methods Prog. Biomed. 2016, 127, 144-164. [CrossRef] [PubMed] otwiera się w nowej karcie
  98. Daamouche, A.; Hamami, L.; Alajlan, N.; Melgani, F. A wavelet optimization approach for ECG signal classification, Biomed. Signal Process. Control 2012, 7, 342-349. [CrossRef] otwiera się w nowej karcie
  99. Kutlu, Y.; Kuntalp, D. Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients. Comput. Method Prog. Biomed. 2012, 105, 257-267. [CrossRef] [PubMed] otwiera się w nowej karcie
  100. Mitra, M.; Samanta, R.K. Classification of ECG arrythmia beats with artificial neural networks. Procedia Technol. 2013, 10, 76-84. [CrossRef] otwiera się w nowej karcie
  101. Dursch, A.; Yen, D.C.; Shih, D.H. Bluetooth technology: An exploratory study of the analysis and implementation frameworks. Comput. Stand. Interface 2004, 26, 263-277. [CrossRef] otwiera się w nowej karcie
  102. Khoso, M.; Khan, K. Smartphones: A Supercomputer in Your Pocket, Analytics Trends. Northeastern University, 2016. Available online: supercomputer-in-your-pocket/ (accessed on 13 September 2017). otwiera się w nowej karcie
  103. Miao, F.; Cheng, Y.; He, Y.; He, Q.; Li, Y. A Wearable Context-Aware ECG Monitoring System Integrated with Built-in Kinematic Sensors of the Smartphone. Sensors 2015, 15, 11465-11484. [CrossRef] [PubMed] otwiera się w nowej karcie
  104. Gao, H.; Duan, X.; Guo, X.; Huang, A.; Jiao, B. Design and tests of a smartphones-based multi-lead ECG monitoring system. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Osaka, Japan, 3-7 July 2013.
  105. Gradl, S.; Kugler, P.; Lohmuller, C.; Eskofier, B. Real-time ECG monitoring and arrhythmia detection using Android-based mobile devices. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), San Diego, CA, USA, 28 August-1 September 2012. otwiera się w nowej karcie
Politechnika Gdańska

wyświetlono 138 razy

Publikacje, które mogą cię zainteresować

Meta Tagi