راهکارهای تشخیص عملکرد کاربران و ارائه روشی برای تعیین میزان خطر رفتار غیر معمول کاربران در خانه‌ی هوشمند مبتنی بر منطق فازی

نوع مقاله : مقاله پژوهشی

نویسندگان

دانشکده برق و کامپیوتر، گروه مهندسی کامپیوتر، دانشگاه صنعتی قم، قم، ایران.

چکیده

در سال‌های اخیر، جمعیت افراد بیمار و سالمند که تنها هستند و نیاز به مراقبت دارند، افزایش یافته‌است. همین مسئله احتیاج به داشتن خانه‌ی هوشمند برای باخبر بودن از شرایط بیمار را افزایش می‌دهد. شناسایی فعالیت بیمار با استفاده از حسگرهای تعبیه شده در محیط، اولین قدم برای رسیدن به خانه‌ی هوشمندی است که در آن اطرافیان بیمار می‌توانند با نگرانی کمتری، بیمار را در خانه تنها بگذارند. در این پژوهش، انواع روش‌های تشخیص عملکرد کاربران در خانه‌ی هوشمند ارائه می‌شود و پس از آن به روش جدیدی برای شناسایی میزان خطر که در آن از منطق فازی در مواردی مثل زمان شروع فعالیت استفاده شده، پرداخته می‌شود. سیستم استفاده شده برای تشخیص میزان خطر، در سه فاز از منطق فازی استفاده کرده است. در این روش به دلیل آنکه برای افراد با شرایط خاص پیاده‌سازی شده‌است، از حسگرهای پوششی استفاده نشد زیرا اگر از سنسورهای پوشیدنی برای بررسی بروز این مشکل استفاده شود، برای سالمند سختی به همراه دارد و حتی یک فرد آلزایمری ممکن است فراموش کند آن را بپوشد که با توجه به این شرایط، پیاده‌سازی این لایه نیز نتایج خوبی – یعنی دقت 84% - را بدست آورد.

کلیدواژه‌ها


عنوان مقاله [English]

Approaches of user activity detection and a new fuzzy logic-based method to determine the risk amount of user unusual activity in the smart home

نویسندگان [English]

  • Hanie Abbasi
  • Mahboobeh Shamsi
  • Abdolreza Rasuli Kenari
Faculty of Electrical and Computer Engineering, Department of Computer Engineering, Qom University of Technology, Qom, Iran.
چکیده [English]

In recent years, the number of sick and elderly people that are living alone in their homes and need care has grown. This is why the smart home is needed for the awareness of their condition. Identification of the patient's activity using environment sensors is the first step in implementing a smart home. In such a home, the patient's relatives can leave the patient alone with less concern. In this research, various methods of recognizing user’s activity in the smart home are presented, and then a new method is introduced to diagnose the risk amount of Alzheimer's patients in which fuzzy-logic is used in cases such as start-up time of the activity. This system uses fuzzy logic in three phases. Since this method is considered for specific patients, the wearing sensors are not used because they have difficulties for the elderly and even an Alzheimer's patient may forget to wear them. However, the implementation of this layer also achieved good results, i.e., 84% accuracy.

کلیدواژه‌ها [English]

  • Smart home
  • Activity identification
  • Elderly people
  • Alzheimer’s disease
  • Fuzzy logic
[1] World Health Organization. 10 facts on ageing and health. 2017; Available from: http://www.who.int/features/factfiles/ageing/en/.
[2] Alzheimer's Association Report, Alzheimer's disease facts and figures, Alzheimer's & dementia, 9(2):208-245, 2013.
[3] World Health Organization, 10 facts on ageing and the life course. 2015; Available from: http://www.who.int/features/factfiles/ageing/ageing_facts/en/index.html.
[4] Allen K., Deaths From Falls by Older Adults Sharply Increase. 2018; Available from: https://www.aarp.org/health/conditions-treatments/info-2018/falling-deaths-surge-for-elderly.html.
[5] Shakeri S., “A Smartphone-based Fall Detection System using Accelerometer and Microphone”, Iranian Journal of Biomedical Engineering, 9(4):399-410, 2016.
[6] Gayathri K., Elias S., and Ravindran B., “Hierarchical activity recognition for dementia care using Markov Logic Network”, Personal and Ubiquitous Computing, 19(2):271-285, 2015.
[7] Gayathri K. and Easwarakumar K., “Intelligent decision support system for dementia care through smart home”, Procedia Computer Science, 93:947-955, 2016.
[8] Hossain M.M., Fotouhi M., and Hasan R., “Towards an Analysis of Security Issues, Challenges, and Open Problems in the Internet of Things”, in 2015 IEEE World Congress on Services. 2015. New York, NY, USA: IEEE.
[9] Sukanya P. and Gayathri K. S., “An Unsupervised Pattern Clustering Approach for Identifying Abnormal User Behaviors in Smart Homes”, IJCSN, 2(3), 2013.
[10] Bakar U.A.B.U.A., Ghayvat H., Hasanm S.F., and Mukhopadhyay S.C., “Activity and anomaly detection in smart home: A survey”, in Next Generation Sensors and Systems, Springer. pp. 191-220, 2016.
[11] Damasevicius R., Vasiljevas M., Salkevicius J., and Wozniak M., “Human activity recognition in AAL environments using random projections”, Computational and mathematical methods in medicine, 2016.
[12] Lapalu J., Bouchard K., Bouzouane A., Bouchard B., and Giroux S., “Unsupervised Mining of Activities for Smart Home Prediction”, Procedia Computer Science, 19:503-510, 2013.
[13] Bouchard K., “Unsupervised spatial data mining for human activity recognition based on objects movement and emergent behaviors”, Université du Québec à Chicoutimi, 2014.
[14] Fahad L.G., Tahir S.F., and Rajarajan M., “Activity Recognition in Smart Homes Using Clustering Based Classification”, 22nd International Conference on Pattern Recognition. 2014.
[15] Fleury A., Vacher M., and Noury N., “SVM-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results”, IEEE transactions on information technology in biomedicine, 14(2):274-283, 2010.
[16] Medjahed H., Istrate D., Boudy J., and Dorizzi B., “Human activities of daily living recognition using fuzzy logic for elderly home monitoring”, in Fuzzy Systems, FUZZ-IEEE 2009. IEEE International Conference on. IEEE, 2009.
[17] Kim E., Helal S., and Cook D., “Human activity recognition and pattern discovery”, IEEE Pervasive Comput, 9(1): 48-53, 2010.
[18] Lotfi A., Langensiepen C.S., Mahmoud S.M., and Akhlaghinia M.J., “Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour”, J. Ambient Intell. Humaniz. Comput. 3(3): 205-218, 2012.
[19] Hsueh Y.L., Lin N.H., Chang C.C., Chen O. T.-C., and Lie W.N., “Abnormal event detection using Bayesian networks at a smart home”, in Ubi-Media Computing (UMEDIA), 2015 8th International Conference on. IEEE, 2015.
[20] Casagrande F.D. and Zouganeli E., “Activity Recognition and Prediction in Real Homes”, in Symposium of the Norwegian AI Society. Springer, 2019.
[21] Kashyap V.S., “Activity recognition and resident identification in smart home environment”, Unitec Institute of Technology, Auckland, New Zealand, 2020.
[22] Mohmed G., Lotfi A., and Pourabdollah A., “Human activities recognition based on neuro-fuzzy finite state machine”, Technologies, 6(4):110, 2018.
[23] Ramapatruni S., Narayanan S. N., Mittal S., Joshi A., and Joshi K. P., “Anomaly detection models for smart home security”, in IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing (HPSC), and IEEE Intl Conference on Intelligent Data and Security (IDS) 2019.
[24] Yamauchi M., Ohsita Y., Murata M., Ueda K., and Kato Y., “Anomaly detection for smart home based on user behavior”, in IEEE International Conference on Consumer Electronics (ICCE). 2019.
[25] Yamauchi M., Ohsita Y., Murata M., Ueda K., and Kato Y., “Anomaly Detection in Smart Home Operation from User Behaviors and Home Conditions”, IEEE Transactions on Consumer Electronics, 66(2):183-192, 2020.
[26] Cardinaux F., Brownsell S., Hawley M., and Bradley D., “Modelling of behavioural patterns for abnormality detection in the context of lifestyle reassurance”, springer, Iberoamerican Congress on Pattern Recognition, pp. 243-251, 2008.
[27] Forkan A.R.M., Khalil I., Tari Z., Foufou S., and Bouras A., “A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living”, Pattern Recognition, 48(3):628-641, 2015.
[28] Khan W.A., Hussain M., Afzal M., Amin M. B., and Lee S. “Healthcare standards based sensory data exchange for Home Healthcare Monitoring System”, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012.
[29] Lee M.S., Lim J.G., Park K.R., and Kwon D.S., “Unsupervised clustering for abnormality detection based on the tri-axial accelerometer”, in ICCAS-SICE. pp. 134-137, 2009.
[30] Lee Y.S. and Chung W.Y., “Automated abnormal behavior detection for ubiquitous healthcare application in daytime and nighttime”, in Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics. 2012.
[31] Virone G., Alwan M., Dalal S., Kell S.W., Turner B., Stankovic J.A., and Felder R.A., “Behavioral patterns of older-adults in assisted living”, IEEE Trans Inf Technol Biomed, 12(3):387-98, 2008.
[32] Hou T.H.T., Liu W.L., and Lin L., “Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets”, Journal of Intelligent Manufacturing, 14(2):239-253, 2003.
[33] Barsocchi P., Cimino M.G., Ferro E., Lazzeri A., Palumbo F., and Vaglini G., “Monitoring elderly behavior via indoor position-based stigmergy”, Pervasive and Mobile Computing, 23:26-42, 2015.
[34] Rashidi P., Cook D.J., and Holder L.B., “Schmitter-Edgecombe M., Discovering activities to recognize and track in a smart environment”, IEEE transactions on knowledge and data engineering, 23(4):527-539, 2010.
[35] Yan Q., Xia S., and Shi Y., “An anomaly detection approach based on symbolic similarity”, in Chinese Control and Decision Conference. 2010.
[36] Roth E., 7 Tips for Reducing Sundowning. 2016; Available from: https://www.healthline.com/health/dementia-sundowning#take-care-ofyourself.
[37] Mokhtari G., Zhang Q., and Fazlollahi A., “Non-wearable UWB sensor to detect falls in smart home environment”, in Pervasive Computing and Communications Workshops (PerCom Workshops), pp. 274-278. IEEE, 2017.
[38] Assocaiation; Available from: https://iranalz.ir/site/index.
[39] CASAS, t.W.C.s.h. project, Editor. 2011.