یک معماری محاسبات هوشمند در اینترنت اشیاء پزشکی جهت کاهش تاخیر سیستم نظارت مستمر بیماران کم¬توان حرکتی و بیماران خاص
محورهای موضوعی : مهندسی کامپیوتر و فناوری اطلاعات
رضا آریانا
1
,
محمدرضا مجمع
2
,
سمیه جعفرعلی جاسبی
3
1 - گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
2 - گروه مهندسی کامپیوتر، واحد پردیس، دانشگاه آزاد اسلامی، پردیس، ایران
3 - گروه مهندسی کامپیوتر، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران
کلید واژه: تشخیص انجماد راه رفتن (FOG), بیماری پارکینسون (PD),
چکیده مقاله :
فناوری اینترنت اشیاء (IoT) یک رویکرد ساختاریافته برای رسیدگی به جنبه¬های ارائه خدمات مراقبت¬های بهداشتی از نظر سلامت و نظارت از راه¬دور برای بیماران دارای شرایط خاص و بیمار¬های تهدیدکننده زندگی ارائه می¬دهد. اینترنت اشیاء حجم بی¬سابقه ای از داده را تولید می¬کند که می¬تواند با استفاده از محاسبات ابری پردازش شود که به دلیل محدودیت منابع ،تاخیر بسیار زیادی را به دنبال خواهد داشت. اما برای برنامه¬های نظارت بر سلامت از راه دور بی¬درنگ، تأخیر ناشی از انتقال داده¬ها به ابر و بازگشت به برنامه غیرقابل قبول است. در این مقاله نظارت از راه دور سلامت بیمار در خانه¬های هوشمند با استفاده از مفهوم محاسبه مه در دروازه هوشمند پیشنهاد شده است. سیستم تشخیص FOG پیاده‌سازی‌شده تحت محاسبات مه، شامل یک نگاشت خطی و نگاشت موبیوس در ترکیب با منطق فازی برای ایجاد خروجی چند سطحی(MLFM-Map) بود که از وضوح‌های فضایی مختلف در تجزیه و تحلیل داده‌های حرکتی بهره‌برداری می¬کند. رویکرد پیشنهاد شده عملکرد طبقه‌بندی خوب تا عالی را نشان داد، با دقت بیش از 90٪ از قسمت‌های FOG به طور متوسط با تاخیر بسیار کم در مجموعه داده اصلی شناسایی شد.
Internet of Things (IoT) technology offers a structured approach to address aspects of health care delivery in terms of health and remote monitoring for patients with specific conditions and life-threatening diseases. The Internet of Things will generate an unprecedented amount of data that can be processed using cloud computing, which will result in huge delays due to resource limitations. But for real-time remote health monitoring applications, the delay caused by transferring data to the cloud and back to the application is unacceptable. we proposed remote monitoring of patient health in smart homes using the concept of fog computing in smart gateway. The FOG detection system implemented under fog computing consisted of a linear map and a Mobius map in combination with fuzzy logic to create a multi-level output (MLFM-map) that exploits different spatial resolutions in motion data analysis. The model architecture and parameters are designed to provide optimal performance while reducing computational complexity and testing time. The proposed approach showed good to excellent classification performance, with an accuracy of more than 90% of FOG episodes detected on average with very low latency in the original dataset
1. Joyia, Gulraiz J., Rao M. Liaqat , Aftab Farooq, and Saad Rehman. "Internet of Medical Things (IOMT): applications, benefits and future challenges in healthcare domain." J Commun 12, no. 4 (2017): 240-247.
2. Dilawar, N., Rizwan, M., Ahmad, F., & Akram, S. (2019). Blockchain: securing internet of medical things (IoMT). International Journal of Advanced Computer Science and Applications, 10(1).
3. A. Elbaz, J. H. Bower, D. M. Maraganore, S. K. McDonnell, B. J. Peterson, J. E. Ahlskog, et al.,Risk tables for parkinsonism and Parkinson's disease. Journal of clinical epidemiology.55:25-31, 2002.
4. Elke Heremans, Alice Nieuwboer, and Sarah Vercruysse. Freezing of gait in parkinson’s disease: Where are we now? Current Neurology and Neuroscience Reports, 13(6):350, Apr 2013.
5. B. T. Cole, S. H. Roy, and S. H. Nawab, Detecting freezing-of-gait during unscripted and unconstrained activity. Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 5649-5652, 2011.
6. Q.W.Oung, M. Hariharan, H.L.Lee, S.N.Basah, S.Yaacob, M.Sarillee, et al., Objective assessment of Parkinson's disease symptoms severity: A review. Biomedical Engineering (ICoBE), 2015 2nd International Conference on. 1-6, 2015.
7. Q.W.Oung, M.Hariharan, S.N.Basah, S.Yaacob, M.Sarillee, and H.L. Lee, Use of technological tools for Parkinson's disease early detection: A review. Control System, Computing and Engineering (ICCSCE), 2014 IEEE International Conference on. 343-348, 2014.
8. Q.W.Oung, H.Muthusamy, H.L.Lee, S.N.Basah, S. Yaacob, M.Sarillee, et al., Technologies for assessment of motor disorders in Parkinson’s disease: a review.Sensors.15:21710-21745, 2015.
9. Yıldırım, E., Cicioğlu, M., & Çalhan, A. (2023). Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring. Medical & Biological Engineering & Computing, 61(5), 1133-1147.
10. Nimisha Ghosh, Indrajit Banerjee, IoT-based Freezing of Gait detection using, Grey Relational Analysis, Internet of Things (2019), doi: https://doi.org/10.1016/j.iot.2019.100068.
11. Verma, P., & Sood, S. K. (2018). Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet of Things Journal, 5(3), 1789-1796. 12. Pardoel S, Nantel J, Kofman J and Lemaire ED (2022) Prediction of Freezing of Gait in Parkinson’s Disease Using Unilateral and Bilateral Plantar-Pressure Data. Front. Neurol. 13:831063. doi: 10.3389/fneur.2022.831063
13. Iranpak, S., Shahbahrami, A., & Shakeri, H. (2021). Remote patient monitoring and classifying using the internet of things platform combined with cloud computing. Journal of Big Data, 8, 1-22. https://doi.org/10.1186/s40537-021-00507-w.
14. Bandopadhaya S, Dey R, Suhag A, Integrated Healthcare, Monitoring Solutions for Soldier using the Internet of Things with Distributed Computing,Sustainable Computing: Informatics and Systems (2020), doi: https://doi.org/10.1016/j.suscom.2020.100378.
15. Khan, J. S., Tahir, A., Ahmad, J., Shah, S. A., Abbasi, Q. H., Russell, G., & Buchanan, W. (2020, July). 5G-FOG: Freezing of gait identification in multi-class softmax neural network exploiting 5G spectrum. In Science and Information Conference (pp. 26-36). Cham: Springer International Publishing.
16. Abbasi, M., Mohammadi-Pasand, E., & Khosravi, M. R. (2021). Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing. Computer Communications, 169, 71-80.
17. Oung, Q. W., Basah, S. N., Muthusamy, H., Vijean, V., Lee, H., Khairunizam, W., ... & Ibrahim, Z. (2018, August). Objective evaluation of freezing of gait in patients with Parkinson's Disease through machine learning approaches. In 2018 International Conference on Computational Approach in Smart Systems Design and Applications (ICASSDA) (pp. 1-7). IEEE.
18. Iranpak, S., Shahbahrami, A., & Shakeri, H. (2022). Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing.
19. Camps, J., Sama, A., Martin, M., Rodriguez-Martin, D., Perez-Lopez, C., Arostegui, J. M. M., ... & Rodriguez-Molinero, A. (2018). Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit. Knowledge-Based Systems, 139, 119-131.
20. Ashour, A. S., El-Attar, A., Dey, N., Abd El-Naby, M. M., & Abd El-Kader, H. (2018, December). Patient-dependent freezing of gait detection using signals from multi-accelerometer sensors in Parkinson’s disease. In 2018 9th cairo international biomedical engineering conference (CIBEC) (pp. 171-174). IEEE.
21. Li, B., et al. Automatic Detection System for Freezing of Gait in Parkinson's Disease Based on the Clustering Algorithm. in 2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC). 2018.
22. Mikos, V., et al. A Neural Network Accelerator With Integrated Feature Extraction Processor for a Freezing of Gait Detection System. In 2018 IEEE Asian Solid-State Circuits Conference (A-SSCC).
23. Mohammadian Rad, N., et al., Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson's Disease and Autism Spectrum Disorders. Sensors (Basel), (2018).
24. El-Attar, A., et al., Hybrid DWT-FFT Features for Detecting Freezing of Gait in Parkinson's Disease, in Information Technology and Intelligent Transportation Systems. , IOS Press. p117-126.
25. Punin, C., et al., A non-invasive medical device for parkinson’s patients with episodes of freezing of gait. Sensors, 2019.19 (3): p737.
26. Mazzetta, I., et al., Wearable sensors system for an improved analysis of freezing of gait in Parkinson’s disease using electromyography and inertial signals. Sensors, 2019. 19(4): p.948.
27. van den Berg, M. and A. Maeder, Prediction of freezing of gait in patients with Parkinson’s disease using EEG signals. Telehealth for Our Ageing Society: Selected Papers from Global Telehealth ,2017.2018. 246 : p.124.
28. Torvi, V.G., A. Bhattacharya, and S. Chakraborty. Deep domain adaptation to predict freezing of gait in patients with Parkinson's disease. in 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).2018. IEEE.
29. Novák, V.; Perfilieva, I.; Močkoř, J. (1999). Mathematical principles of fuzzy logic. Dordrecht: Kluwer Academic. ISBN 978-0-7923-8595-0.
30. ^ "Fuzzy Logic". Stanford Encyclopedia of Philosophy. Bryant University. 23 July 2006. Retrieved 30 September 2008.
31. ^ Zadeh, L. A. (June 1965). "Fuzzy sets". Information and Control. San Diego. 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X. ISSN 0019-9958. Wikidata Q25938993.
32. ^ Pelletier, Francis Jeffry (2000). "Review of Metamathematics of fuzzy logics" (PDF). The Bulletin of Symbolic Logic. 6 (3): 342–346. doi:10.2307/421060. JSTOR 421060. Archived (PDF) from the original on 3 March 2016.
33. ^ "What is Fuzzy Logic? "Mechanical Engineering Discussion Forum"". mechanicalsite.com. Archived from the original on 11 November 2018. Retrieved 11 November 2018.
34. ^ Babuška, Robert (1998). Fuzzy Modeling for Control. Springer Science & Business Media. ISBN 978-94-011-4868-9.
35. Mamdani, E. H. (1974). "Application of fuzzy algorithms for control of simple dynamic plant". Proceedings of the Institution of Electrical Engineers. 121 (12): 1585–1588. doi:10.1049/PIEE.1974.0328.
36. Takagi, Tomohiro; Sugeno, Michio (January 1985). "Fuzzy identification of systems and its applications to modeling and control". IEEE Transactions on Systems, Man, and Cybernetics. SMC-15 (1): 116–132. doi:10.1109/TSMC.1985.6313399. S2CID 3333100.
37. S. Mirjalili, A.H. Gandomi, S.Z. Mirjalili, S. Saremi, H. Faris, S.M. Mirjalili, Salp swarm algorithm: a bio-inspired optimizer for engineering design problems, Adv. 38. M. Mirrashid and H. Naderpour, “Transit search: An optimization algorithm based on exoplanet exploration,” Results Control Optim., vol. 7, p. 100127, Jun. 2022, doi: 10.1016/j.rico.2022.100127.
M. Bachlin, M. Plotnik, D. Roggen, I. Maidan, J. M. Hausdorff, N. Giladi, and G. Troster. Wearable assistant for parkinson’s disease patients with the freezing of gait symptom. IEEE Transactions on Information Technology in Biomedicine, 14(2):436–446, March 2010.