Akıllı ve Geleneksel Giyilebilir Sağlık Cihazlarında Nesnelerin İnterneti

İnternetin 1990’lı yılların sonuna doğru insanların yaşamına girmesi ile dünyanın herhangi bir yerindeki bir cihazla başka bir cihazın birbirleriyle iletişim kurması mümkün hale gelmiştir. İnternet teknolojisinin 2000’li yılların başında olağanüstü gelişimini akıllı mobil teknolojilerinin (akıllı telefon, saat, gözlük ve diğer düşük güçlü giyilebilir ve takılabilir cihazlar) büyük bir hızla gelişmesi takip etmiştir. Bu akıllı mobil teknolojilere entegre edilen sensörlerden faydalanılarak bireyin bulunduğu ortamdan birçok farklı verinin elde edilmesi sağlanmıştır. Elde edilen bu veriler, kablolu veya kablosuz olarak internet yoluyla bir merkezde toplanıp, incelenip,  analiz edilmiştir. Bu sayede cihaza sahip kişi veya cihazın bulunduğu ortam hakkında çeşitli bilgilere kısa sürede ulaşılmıştır. Yaşanan bu gelişmeler internet üzerinden nesnelerin birbiriyle haberleşmesi(IoT) fenomenini ortaya çıkarmıştır. IoT ile ilgili çok kapsamlı araştırmalar ve uygulamalar günümüzde çeşitli alanlarda devam etmektedir. IoT’ un en çok kullanıldığı alanlardan birisi de sağlık hizmetleri alanıdır. Hastalıkların doğru teşhisi, tedavisi ve takibinde özellikle hastanın hastane dışındaki günlük yaşantısından alınacak veriler büyük bir önem taşımaktadır. Bu verileri elde etmenin en iyi yolu IoT giyilebilir veya takılabilir sağlık cihazlarını kullanmaktır.  Bu çalışmanın amacı,  şimdiye kadar yapılan IoT tabanlı geleneksel ve akıllı sistem olarak yapılan giyilebilir ve takılabilir sağlık cihazı uygulamlarından elde edilen bulguları özetlemektir. Bu bulgular ışığında da IoT tabanlı uygulamaların geleceği hakkında temel sorunları ele alarak çeşitli öneriler getirmektir.

Internet of Things in Smart and Conventional Wearable Healthcare Devices

With the Internet entering the lives of people towards the end of the 1990s, it became possible for devices anywhere in the world to communicate with each other. At the beginning of the 2000s, Internet technology was followed by the rapidly development of smart mobile technology. By using the sensors integrated in these intelligent mobile technologies, it was possible to obtain many different data from the environment of the individual. The data that obtained via wired or wireless internet then collected and analyzed by a center. In this way, various information about the environment in which the person or device is located and can be reached in a short time. These developments reveal the phenomenon that things communicate with each other over the internet. Extensive research and applications related to IoT are currently underway in various fields. One of the most used areas of IoT is health care. In diagnosis, treatment and follow-up of the diseases, especially the daily life of the patient outside the hospital is of great importance. The best way to obtain this data is to use IoT wearable or implantable healthcare devices. The aim of this study is to summarize the findings obtained from wearable and implantable health device applications made as conventional and intelligent system based on IoT up to now. In the light of these findings, we will introduce various proposals by addressing the fundamental problems of the future of IoT-based applications.

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  • [1] He W., Goodkind D. and Kowal P., “An Aging World: 2015”, US. Census Breau International Population Reports, 95: 1-16, (2016).
  • [2] Neagu G., Preda Ş. and Stanciu A., “A Cloud-IoT Based Sensing Service for Health Monitoring”, The 6th IEEE International Conference on E-Health and Bioengineering – EHB, Sinaia, 53 – 56, (2017).
  • [3] Qi J., Yang P., Amft O., Dong F. and Xu L., “Advanced internet of things for personalised healthcare systems: A survey”, Pervasive and Mobile Computing, 41: 132–149, (2017).
  • [4] http://www.businessinsider.com/there-will-be-34-billion-iot-devices-installed-on-earth-by-2020 2016-5, “There will be 24 billion IoT devices installed on Earth by 2020” , (Accesed 29 Feb 2018).
  • [5] Laplante P.A. and Laplante N., “The Internet of Things in Healthcare Potential Applications and Challenges”, IT Pro, 18: 2 – 4, (2016)
  • [6] Roggen D., Perez D.G., Fukumoto M. and Laerhoven K.V., “Wearables Are Here to Stay”, IEEE 17thWearable Computer Symposium (ISWC), 13: 14 –18, (2014).
  • [7] Bonato P., "Wearable sensors/systems and their impact on biomedical engineering" IEEE Engineering in Medicine and Biology Magazine, 22: 18-20, (2003).
  • [8] Baber C., “Can Wearables Be Wıreable?”, Antennas and Propagation for Body-Centric Wireless Communications, IET Seminar, London, 13-18, (2007).
  • [9] https://www.ftc.gov/news-events/contests/iot-rules, “Federal Trade Comission” (2018).
  • [10] Geng H., “IPv6 for Iot and Gateway”, Internet of Things and Data Analytics Handbook, Wiley Telecom, 816, (2017).
  • [11] Lo B.P.L., Ip H. and Yang G.-Z., “Transforming Health Care”, IEEE Pulse, 7: 4-8, (2016).
  • [12] Khan S.F., “Health Care Monitoring System in Internet of Things (loT) by Using RFID”, The 6th International Conference on Industrial Technology and Management, Cambridge, 198 – 204, (2017).
  • [13] Lebepe F., Niezen G., Hancke G.P. and Ramotsoela T.D., “Wearable stress monitoring system using multiple sensors”, IEEE 14th International Conference on Industrial Informatics (INDIN), Poitiers, 895–898, (2016).
  • [14] https://cordis.europa.eu/project/rcn/93799_es.html, “OPTIMI Project”, (2018).
  • [15] Majoe D., Bonhof P., Kaegi-Trachsel T., Gutknecht J. and Widmer L., “Stress and Sleep Quality Estimation from a Smart Wearable Sensor”, 5th International Conference on Pervasive Computing and Applications, Maribor, 14-19, (2010).
  • [16] Alexander A. and Arun C.S., “Mobıle ECG Monıtorıng Devıce Usıng Wearable Non Contact Armband”, International Conference on circuits Power and Computing Technologies, Kollam, 1-4, (2017).
  • [17] D. Yotha, C.Pidthalek, S. Yimman and Niramitmahapanya S., “ Design and Construction of the Hypoglycemia Monito Wireless System for Diabetic”, Biomedical Engineering International Conference, Laung Prabang, 1-4, ( 2016)
  • [18] http://www.libelium.com/130220224710/, “e-Health Sensor Platform for Biometric and Medical applications”, (2018).
  • [19] Kaplan M., Berk T.N., Çemrek B., Şahin S. and Fidan U., “Mobıle Physıologıcal Sıgnal Monıtorıng System for Famıly Medıcıne”, Medical Technologies National Congress, Trabzon, 1-4, (2017).
  • [20] Kang J. J., Luan T.H. and Larkin H., “Inference System of Body Sensors for Health and Internet of Things Networks”, 14th International Conference, Singapore, 94-98, (2016).
  • [21] Benadda B., Beldjilali B., Mankouri A. and Taleb O. “Secure IoT solution for wearable health care applications, case study Electric Imp development platform”, International Journal of Communication System, 31: 5 , (2018).
  • [22] Jha V., Prakash N. and Sagar S., “Wearable Anger-Monitoring System” ICT Express, 3: 3, (2017).
  • [23] https://www.nih.gov/, “National Istitute of Health”, (2018).
  • [24] Santhi V., Ramya K., Tarana A.P.J. and Vinitha G., “IOT Based Wearable Health Monitoring System for Pregnant Ladies Using CC3200”, International Journal of Advanced Research Methodology in Engineering & Technology, 1: 3, (2017).
  • [25] Delrobaei M., Memar S., Pieterman M., Stratton T.W., McIsaac K. and Jog M., “Towards Remote Monitoring of Parkinson’s Disease Tremor Using Wearable Motion Capture Systems”, Journal of the Neurological Sciences, 384: 38-45, (2018).
  • [26] Rahimi F., Bee C., Debicki D., Roberts A.C., Bapat P. and Jog M., “Effectiveness of boNT An in Parkinson’s disease upper limb tremor management”. Canadian Journal of Neurologic Science, 40: 663–669, (2013).
  • [27] Grimaldi G. and Manto M., “Tremor from Pathogenesis to Treatment, Morgan and Claypool”, San Rafael, CA USA, (2008).
  • [28] Zwarts M.J., Drost G. and Stegeman D.F., “Recent progress in the diagnostic use of surface EMG for neurological diseases”, Journal of Electromyography and Kinesiology, 10(5): 287–291, (2000).
  • [29] Spieker S., Ströle V., Sailer A., Boose A., Dichgans J., “Validity of long-term electromyography in the quantification of tremor”, Movement Disorders, 12(6): 985–991, (1997).
  • [30] Foerster F. and Smeja M., “Joint amplitude and frequency analysis of tremor activity”, Electromyography and Clinical Neurophysiology, 39(1): 11–19, (1999).
  • [31] Salarian A., Russmann H., Wider C., Burkhard P.R., Vingerhoets F.J.G. and Aminian K., “Quantification of tremor and bradykinesia in Parkinson’s disease using a novel ambulatory monitoring system”, IEEE Transactions Biomedical Engineering, 54(2): 313–322, (2007).
  • [32] Rahimi F., Duval C., Jog M., Bee C., South A., Jog M., Edwards R. and Boissy P.,”Capturing whole-body mobility of patients with Parkinson disease using inertial motion sensors: expected challenges and rewards”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, 5833-5838, (2011).
  • [33] Rahimi F., Bee C., South A., Debicki D. and Jog M., “Variability of hand tremor in rest and in posture a pilot study”, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, 470–473, (2011) .
  • [34] Chen B-R., Shyamal P., Buckley T., Rednic R., McClure D.J., Shih L., Tarsy D., Welsh M. and Bonato P., “A Web-Based System for Home Monitoring of Patients With Parkinson’s Disease Using Wearable Sensors”, IEEE Transactıons On Biomedical Engineering, 58(3): 831-836, (2011).
  • [35] Kuusik A., Alam M.M., Kask T. and Gross-Paju K., “Wearable M-Assessment System for Neurological Disease Patients”, IEEE 4th World Forum on Internet of Things (WF-IoT), Singapore, 201-206, (2018).
  • [36] Szczęsna A., Nowak A., Grabiec P., Paszkuta M. and Tajstra, M., “Survey of Wearable Multi-modal Vital Parameters Measurement Systems Innovations in Biomedical Engineering” Springer International Publishing, Cham, 323-329, (2017).
  • [37] https://www.mstrust.org.uk/a-z/expanded-disability-status-scale-edss, “Expanded Disability Status Scale (EDSS)”, (2018).
  • [38] Huang C.H. and Cheng K.W., “Rfid technology combined with iot application in medical nursing system”, Bulletin of Networking, Computing, Systems, and Software, 3(1): 20-24, (2014).
  • [39] Otto C., Milenkovic A., Sanders C. and Jovanov E., “System architecture of a wireless body area sensor network for ubiquitous health monitoring”, Journal of Mobile Multimedia, 1( 4): 307-326, (2006).
  • [40] Lukowicz P., Anliker U., Ward J., Troster G., Hirt E., Neufelt C., “Amon: A wearable medical computer for high risk patients”, 6th International Symposium on, Seattle, 133-134, (2002).
  • [41] Milenkovic A, Otto C., Jovanov E., “Wireless sensor networks for personal health monitoring: Issues and animplementation”, Computer Communications, 29(13): 2521-2533, (2006).
  • [42] Woo Woo M., Lee J.W., Park K.H., “A reliable IoT system for Personal Healthcare Devices”, Future Generation Computer Systems, 78: 626–640, (2018).
  • [43] Gia T.N., Rahmani A.M., Westerlund T., Tenhunen L.H., “Fault tolerant and scalable IoT-based architecture for health monitoring”, IEEE Sensors Applications Symposium,Zadar, 1-6 , (2015).
  • [44] Misra S., Gupta A., Krishna P.V., Agarwa H., Obaidat M.S., “An adaptive learning approach for fault-tolerant routing in Internet of things”, IEEE Wireless Communications and Networking Conference, Shanghai, 815–819, (2012).
  • [45] Chaithra S., Gowrishankar S., “Study of secure fault tolerant routing protocol for IoT”, International Journal of Science and Research, 5(7): 1833–1838, (2016).
  • [46] Fua Y., Liub J., “System design for wearable blood oxygen saturation and pulse measurement device”, 6th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences, (AHFE), 3: 1187-1194, (2015).
  • [47] Sojuyigbe S., Daniel K., “Wearables/IOT Devices: Challenges and solutions to integration of miniature antennas in close proximity to the Human Body”, IEEE Symposium on Electromagnetic Compatibility and Signal Integrity, Santa Clara, 75-78, (2015).
  • [48] Hooshmand M., Zordan D., Testa D.D., Grisan E., Rossi M., “Boosting the Battery Life of Wearables for Health Monitoring Through the Compression of Biosignals”, IEEE Internet Of Things Journal, 4(5): 1647-1662, (2017).
  • [49] https://archive.ics.uci.edu/ml/datasets/opportunity+activity+recognition#, “MIT-BIH dataset”, (2018).
  • [50] Perez J.M.D., Misa W.B., Tan P.A.C., Robles J., “A wireless Blood Sugar Monitoring System Using Ion-Sensitive Field Effect Transistor”, IEEE Region 10 Conference, Singapore, 1742-1746, (2016).
  • [51] Al-Taee M.A., Al-Nuaimy W., Al-Ataby A., Muhsin Z.J., Abood S.N., “Mobile Health Platform for Diabetes Management Based On The Internet-Of-Things”, IEEE Jordan Conference On Applied Electrical Engineering And Computing Technologies (AEECT), Amman, 1-5, (2015).
  • [52] Chakraborty S., Dasgupta A., Dash P., Routray A., “Development of a wireless wearable electrooculogram recorder for IoT based applications”, IEEE 26th International Symposium on Industrial Electronics (ISIE), Edinburgh, 1991-1995, (2017).
  • [53] Wang R., Abukhalaf Z, Javan-Khoshkholgh A. and Wang T.H.H., Sathar S., Du P., Angeli T.R., Cheng LK., “A Miniature Configurable Wireless System for Recording Gastric Electrophysiological Activity and Delivering High-Energy Electrical Stimulation”, IEEE Journal On Emergıng And Selected Topics In Cırcuıts And Systems, 8(2): 221-229, (2018).
  • [54] Rotariu C., Manta V. and Costin H., “Wireless Remote Monitoring System for Patients with Cardiac Pacemakers”, International Conference and Exposition on Electrical and Power Engineering, Iasi, 845-848, (2012).
  • [55] Agarwal A., Shapero1 A., Rodger D., Humayun M., Tai Y-C. and Emami A., “A Wireless, Low-Drift, Implantable Intraocular Pressure Sensor with Parylene-on-oil Encapsulation”, IEEE Custom Integrated Circuits Conference (CICC), San Diego, 1-4, (2018).
  • [56] Jia Y., Lee B., Mirbozorgi A. A., Ghovanloo M., Khan W. and Li W., “Towards a Free-Floating Wireless Implantable Optogenetic Stimulating System” IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), Boston, 381-384, (2017).
  • [57] Majerus S., Makovey I., Zhui H., Ko1 W. and Damaser M.S., “Wireless Implantable Pressure Monitor for Conditional Bladder Neuromodulation”, IEEE Biomedical Circuits and Systems Conference (BioCAS), Atlanta, 1-4, (2015).
  • [58] Mecheraoui C., Cobb J. and Swain I., “Evaluation of a wireless in-shoe sensor based on ZigBee used for drop foot stimulation”, IEEE Radio and Wireless Symposium, Santa Clara, 423-426, (2012).
  • [59] Lymberis A., “Smart Wearables for Remote Health Monıtorıng, From Preventıon to Rehabılıtatıon: Current R&D”, 4th International IEEE EMBS Special Topic Conference on Information Technology Applications in Biomedicine, Birmingham, 272-275, (2003).
  • [60] Rigas G. and Tzallas A.T., Tsipouras M.G., Bougia P., Tripoliti E.E., Baga D., Fotiadis D.I., Tsouli S.G., Konitsiotis S., “Assessment of tremor activity in the Parkinson ’s disease using a set of wearable sensors”, IEEE Transactions Information Technology Biomedicine , 16(3): 478–487, (2012).
  • [61] Sung M., Marci C. and Pentland A.,"Wearable feedback systems for rehabilitation" Journal of neuroengineering and rehabilitation 2.1, 17: 2-17, (2005).
  • [62] Asthana S., Megahed A. and Strong R., “A Recommendation System for Proactive Health Monitoring Using IoT and Wearable Technologies”, IEEE 6th International Conference on AI & Mobile Services, Honolulu, 14-21, (2017).
  • [63] Davis D.A., Chawla N.V., Nicholas B., Christakis N. and Barabasi A-L., “Predicting individual disease risk based on medical history”, Proceedings of the 17th ACM conference on Information and knowledge management, California, 769-778, (2008).
  • [64] McCormick, Tyler H., Cynthia Rudin, and David Madigan. “Bayesian hierarchical rule modeling for predicting medical conditions”, The Annals of Applied Statistics, 6(2): 652-668, (2012).
  • [65] Choi E., Bahadori M.T., Schuetz A., Stewart W. F. and Sun J., “RETAIN: Interpretable Predictive Model in Healthcare using Reverse Time Attention Mechanism”, Accepted at Neural Information Processing Systems (NIPS), 1: 3504-3512, (2016).
  • [66] Paxton C., Saria S. and Niculescu-Mizil A., “Developing predictive models using electronic medical records: challenges and pitfalls”, AMIA Anunal Symposium Proceeding Archive, Chicago, 1109–1115 (2013).
  • [67] https://catalog.data.gov/dataset?tags=ehr, “Data.Gov”, (2018).
  • [68] Mark H., Frank E., Holmes G., Pfahringer B., Reutemann P. and Witten I.H., “WEKA data mining software: an update”, ACM SIGKDD Explorations Newsletter, 11(1): 10-18, (2009).
  • [69] Zeng M., Nguyen L.T., Yu B., Mengshoel O.J., Zhu J., Wu P. and Zhang J., “Convolutional neural networks for human activity recognition using mobile sensors”, 6th International Conference on Mobile Computing, Applications and Services, Austin, 197–205, (2014).
  • [70] http://www.ife.ee.ethz.ch/research/activity-recognition-datasets.html, “Skoda Mini Checkpoint Dataset”, (2018).
  • [71] https://archive.ics.uci.edu/ml/datasets/opportunity+ activity+recognition, “Opportunity Activity Recognition Dataset”, (2018).
  • [72] http://www.cis.fordham.edu/wisdm/dataset.php, “Actitracker Dataset”, (2018).
  • [73] Yasin M., Tekeste T., Saleh H., Mohammad B., Sinanoglu O. and Ismail M., “Ultra-Low Power Secure IoT Platform for Predicting Cardiovascular Diseases” , IEEE Transactıons On Cırcuıts And Systems–I: Regular Papers, 64(9): 2624-2637, (2017).
  • [74] https://www.physionet.org/physiobank/database/nsrdb/, “PhysioNet NSRDB”, (2018).
  • [75] https://www.physionet.org/physiobank/database/ahadb/, “American Heart Association ECG Database”, (2018),
  • [76] Rav’I D., Wong C., Lo B. and Yang G-Z., “Deep learning for human activity recognition: A resource efficient implementation on low-power devices”, 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, 71–76, (2016).
  • [77] Kwapisz J. R., Weiss G. M. and Moore S. A., “Activity recognition using cell phone accelerometers” ACM SIGKDD Explorations Newsletter, 12(2): 74–82, (2011).
  • [78] Rav’I D., Wong C., Lo B. and Yang G-Z., “A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices”, IEEE Journal of Biomedical and Health Informatics, 21(1): 56-64, (2017).
  • [79] http://hamlyn.doc.ic.ac.uk/activemiles/, “ActiveMiles Dataset”, (2018).
  • [80] https://archive.ics.uci.edu/ml/datasets/Daphnet+Freezing+of+ Gait, “Daphnet Fog Dataset”, (2018).
  • [81] Lockhart J. W., Weiss G. M., Xue J. C., Gallagher S. T., Grosner A.B. and Pulickal T. T., “Design considerations for the WISDM smart phone-based sensor mining architecture” SensorKDD '11 Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, California, 25–33, (2011).
  • [82] Weiss G. M. and Lockhart J. W., “The impact of personalization on smartphone-based activity recognition” AAAI Workshop Activity Context Representation: Techniques and Languages Report, Chicago, 98–104, (2012).
  • [83] Yong B., Xu Z., Wang X., Cheng L., Li X., Wua X. and Zhou Q., “IoT-based intelligent fitness system”, Journal of Parallel and Distributed Computing, 118(1): 14-21, (2017).
  • [84] http://www.nada.kth.se/cvap/actions/, “KTH Dataset”, (2018).
  • [85] Sood S.K. and Mahajan I., “Wearable IoT sensor based healthcare system for identifying and controlling chikungunya virus”, Computers in Industry, 91: 33–44, (2017).
  • [86] Lopez H.J.D., Siller M. and Huerta I., “Internet of vehicles: Cloud and fog computing approaches”, International Conference on Service Operations and Logistics, and Informatics (SOLI), Bari, 211-216, (2017).
  • [87] Huang Q., Wang W. and Zhang Q., “Your Glasses Know Your Diet: Dietary Monitoring Using Electromyography Sensors”, IEEE Internet of Things Journal, 4(3): 705-712, (2017).
  • [88] https://www.thevisioncouncil.org/, “VisionWatch Canada Market Overview”, (2015).
  • [89] Pandey P. and Prabhakar R., “An analysis of machine learning techniques (J48 & AdaBoost)-for classification”, First India International Conference on Information Processing (IICIP), Delhi, 1-6, (2016).
  • [90] Shi W.Y. and Chiao J.-C., “Neural Network Based Real-time Heart Sound Monitor Using a Wireless Wearable Wrist Sensor”, IEEE Dallas Circuits and Systems Conference (DCAS), Arlington, 1-4, (2016).
  • [91] https://www.mathworks.com/help/pdf_doc/nnet/nnet_ug.pdf
Politeknik Dergisi-Cover
  • ISSN: 1302-0900
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 1998
  • Yayıncı: GAZİ ÜNİVERSİTESİ