The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection

Nowadays, the care of the elderly has become a social concern. The fall of the elderly has become one of the main factors threatening the health of the elderly. In this paper, we designed a fall detection algorithm based on human joint extraction and object detection.First,yolov4 was used to identify and detect the elderly. Then openpose was used to detect the human joint. Based on the human joint, this paper using Random Forest to classify the status of the elderly, there are three states of the elderly: falling down, lying down and other states. In the detection of a single old man, the accuracy of the model reached 99.3%, the sensitivity and specificity of the model reached 79.3% and 72.1%

Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow

Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision-based system, such as action recognition. The deep learning technique has not been successfully implemented in vision-based fall detection systems due to the requirement of a large amount of computation power and the requirement of a large amount of sample training data. This research aims to propose a vision-based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre-processing of video images. The proposed system consists of the Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting conditions. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40 to 50ms. The proposed system concentrates on decreasing the processing time of fall detection and improving classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using a dynamic optical flow technique, which helps to increase the performance of image pre-processing steps.Comment: 16 page

Reconocimiento de actividades humanas por medio de extracción de características y técnicas de inteligencia artificial: una revisión

Context: In recent years, the recognition of human activities has become an area of constant exploration in different fields. This article presents a literature review focused on the different types of human activities and information acquisition devices for the recognition of activities. It also delves into elderly fall detection via computer vision using feature extraction methods and artificial intelligence techniques. Methodology: This manuscript was elaborated following the criteria of the document review and analysis methodology (RAD), dividing the research process into the heuristics and hermeneutics of the information sources. Finally, 102 research works were referenced, which made it possible to provide information on current state of the recognition of human activities. Results: The analysis of the proposed techniques for the recognition of human activities shows the importance of efficient fall detection. Although it is true that, at present, positive results are obtained with the techniques described in this article, their study environments are controlled, which does not contribute to the real advancement of research. Conclusions: It would be of great impact to present the results of studies in environments similar to reality, which is why it is essential to focus research on the development of databases with real falls of adults or in uncontrolled environments.Contexto: En los últimos años, el reconocimiento de actividades humanas se ha convertido en un área de constante exploración en diferentes campos. Este artículo presenta una revisión de la literatura enfocada en diferentes tipos de actividades humanas y dispositivos de adquisición de información para el reconocimiento de actividades, y profundiza en la detección de caídas de personas de tercera edad por medio de visión computacional, utilizando métodos de extracción de características y técnicas de inteligencia artificial. Metodología: Este manuscrito se elaboró con criterios de la metodología de revisión y análisis documental (RAD), dividiendo el proceso de investigación en heurística y hermenéutica de las fuentes de información. Finalmente, se referenciaron 102 investigaciones que permitieron dar a conocer la actualidad del reconocimiento de actividades humanas. Resultados: El análisis de las técnicas propuestas para el reconocimiento de actividades humanas muestra la importancia de la detección eficiente de caídas. Si bien es cierto en la actualidad se obtienen resultados positivos con las técnicas descritas en este artículo, sus entornos de estudio son controlados, lo cual no contribuye al verdadero avance de las investigaciones. Conclusiones: Sería de gran impacto presentar resultados de estudios en entornos semejantes a la realidad, por lo que es primordial centrar el trabajo de investigación en la elaboración de bases de datos con caídas reales de personas adultas o en entornos no controlados

A review of abnormal behavior detection in activities of daily living

Abnormal behavior detection (ABD) systems are built to automatically identify and recognize abnormal behavior from various input data types, such as sensor-based and vision-based input. As much as the attention received for ABD systems, the number of studies on ABD in activities of daily living (ADL) is limited. Owing to the increasing rate of elderly accidents in the home compound, ABD in ADL research should be given as much attention to preventing accidents by sending out signals when abnormal behavior such as falling is detected. In this study, we compare and contrast the formation of the ABD system in ADL from input data types (sensor-based input and vision-based input) to modeling techniques (conventional and deep learning approaches). We scrutinize the public datasets available and provide solutions for one of the significant issues: the lack of datasets in ABD in ADL. This work aims to guide new research to understand the field of ABD in ADL better and serve as a reference for future study of better Ambient Assisted Living with the growing smart home trend

Comprehensive review of vision-based fall detection systems

Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers

Fall detection system for elderly people using IoT and ensemble machine learning algorithm

[EN] Falls represent a major public health risk worldwide for the elderly people. A fall not assisted in time can cause functional impairment in an elderly and a significant decrease in his mobility, independence, and life quality. In this sense, we propose IoTE-Fall system, an intelligent system for detecting falls of elderly people in indoor environments that takes advantages of the Internet of Thing and the ensemble machine learning algorithm. IoTE-Fall system employs a 3D-axis accelerometer embedded into a 6LowPAN wearable device capable of capturing in real time the data of the movements of elderly volunteers. To provide high efficiency in fall detection, in this paper, four machine learning algorithms (classifiers): decision trees, ensemble, logistic regression, and Deepnets are evaluated in terms of AUC ROC, training time and testing time. The acceleration readings are processed and analyzed at the edge of the network using an ensemble-based predictor model that is identified as the most suitable predictor for fall detection. The experiment results from collection data, interoperability services, data processing, data analysis, alert emergency service, and cloud services show that our system achieves accuracy, precision, sensitivity, and specificity above 94%.Research presented in this article has been partially funded by Horizon 2020 European Project grant INTER-IoT no. 687283, ACTIVAGE project under grant agreement no. 732679, the Escuela Politecnica Nacional, Ecuador, and Secretaria de Educacion Superior Ciencia, Tecnologia e Innovacion (SENESCYT), Ecuador.Yacchirema, D.; Suárez De Puga, J.; Palau Salvador, CE.; Esteve Domingo, M. (2019). Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Personal and Ubiquitous Computing. 23(5-6):801-817. https://doi.org/10.1007/s00779-018-01196-8S801817235-6He W, Goodkind D, Kowal P (2016) U.S. Census Bureau, International Population Reports, P95/16-1, An Aging World: 2015. U.S. Government Publishing Office, Washington, DCBousquet J, Kuh D, Bewick M, Standberg T, Farrell J, Pengelly R, Joel ME, Rodriguez Mañas L, Mercier J, Bringer J, Camuzat T, Bourret R, Bedbrook A, Kowalski ML, Samolinski B, Bonini S, Brayne C, Michel JP, Venne J, Viriot-Durandal P, Alonso J, Avignon A, Ben-Shlomo Y, Bousquet PJ, Combe B, Cooper R, Hardy R, Iaccarino G, Keil T, Kesse-Guyot E, Momas I, Ritchie K, Robine JM, Thijs C, Tischer C, Vellas B, Zaidi A, Alonso F, Andersen Ranberg K, Andreeva V, Ankri J, Arnavielhe S, Arshad H, Augé P, Berr C, Bertone P, Blain H, Blasimme A, Buijs GJ, Caimmi D, Carriazo A, Cesario A, Coletta J, Cosco T, Criton M, Cuisinier F, Demoly P, Fernandez-Nocelo S, Fougère B, Garcia-Aymerich J, Goldberg M, Guldemond N, Gutter Z, Harman D, Hendry A, Heve D, Illario M, Jeande C, Krauss-Etschmann S, Krys O, Kula D, Laune D, Lehmann S, Maier D, Malva J, Matignon P, Melen E, Mercier G, Moda G, Nizinkska A, Nogues M, O’Neill M, Pelissier JY, Poethig D, Porta D, Postma D, Puisieux F, Richards M, Robalo-Cordeiro C, Romano V, Roubille F, Schulz H, Scott A, Senesse P, Slagter S, Smit HA, Somekh D, Stafford M, Suanzes J, Todo-Bom A, Touchon J, Traver-Salcedo V, van Beurden M, Varraso R, Vergara I, Villalba-Mora E, Wilson N, Wouters E, Zins M (2015) Operational definition of active and healthy ageing (AHA): a conceptual framework. J Nutr Health Aging 19(9):955–960Yacchirema DC, Sarabia-Jácome D, Palau CE, Esteve M (2018) A Smart System for sleep monitoring by integrating IoT with big data analytics. IEEE Access, p 1Robie K (2010) Falls in older people: risk factors and strategies for prevention. JAMA 304(17):1958–1959Jrad RBN, Ahmed MD, Sundaram D (2014) Insider Action Design Research a multi-methodological Information Systems research approach. 2014 IEEE Eighth International Conference on Research Challenges in Information Science (RCIS). Marrakech, pp 1–12. https://doi.org/10.1109/RCIS.2014.6861053Chaccour K, Darazi R, El Hassani AH, Andrès E (2017) From fall detection to fall prevention: a generic classification of fall-related systems. IEEE Sensors J 17(3):812–822Min W, Cui H, Rao H, Li Z, Yao L (2018) Detection of human falls on furniture using scene analysis based on deep learning and activity characteristics. IEEE Access 6:9324–9335Ma X, Wang H, Xue B, Zhou M, Ji B, Li Y (2014) Depth-based human fall detection via shape features and improved extreme learning machine. IEEE J Biomed Heal Inform 18(6):1915–1922Yang L, Ren Y, Zhang W (2016) 3D depth image analysis for indoor fall detection of elderly people. Digit Commun Netw 2(1):24–34Mastorakis G, Makris D (2014) Fall detection system using Kinect’s infrared sensor. J Real-Time Image Process 9(4):635–646Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Prog Biomed 117(3):489–501Wang Y, Wu K, Ni LM (2017) WiFall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594Sehairi K, Chouireb F, Meunier J (2018) Elderly fall detection system based on multiple shape features and motion analysis. 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). Fez, pp 1–8. https://doi.org/10.1109/ISACV.2018.8354084Álvarez de la Concepción MÁ, Soria Morillo LM, Álvarez García JA, González-Abril L (2017) Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive Mob Comput 34:3–13Fortino G, Gravina R (2015) Fall-MobileGuard: a smart real-time fall detection system. In: Proceedings of the 10th EAI International Conference on Body Area Networks (BodyNets '15). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering). ICST, Brussels, Belgium, pp 44–50. https://doi.org/10.4108/eai.28-9-2015.2261462Aguiar B, Rocha T, Silva J, Sousa I (2014) Accelerometer-based fall detection for smartphones. 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA). Lisboa, pp 1–6. https://doi.org/10.1109/MeMeA.2014.6860110Kau L, Chen C (2015) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Heal Inform 19(1):44–56He J, Bai S, Wang X (2017) An Unobtrusive Fall Detection and Alerting System Based on Kalman Filter and Bayes Network Classifier. Sensors 17:1393. https://doi.org/10.3390/s17061393Santoyo-Ramón JA, Casilari E, Cano-García JM (2018) Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning. Sensors 18:1155. https://doi.org/10.3390/s18041155Mao A, Ma X, He Y, Luo J (2017) Highly Portable, Sensor-Based System for Human Fall Monitoring. Sensors 17:2096. https://doi.org/10.3390/s17092096Casilari E, Oviedo-Jiménez MA (2015) Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS One 10(11):e0140929Dias PVGF, Costa EDM, Tcheou MP, Lovisolo L (2016) Fall detection monitoring system with position detection for elderly at indoor environments under supervision. 2016 8th IEEE Latin-American Conference on Communications (LATINCOM). Medellin, pp. 1–6. https://doi.org/10.1109/LATINCOM.2016.7811576Phu PT, Hai NT, Tam NT (2015) A Threshold Algorithm in a Fall Alert System for Elderly People. In: Toi V, Lien Phuong T (eds) 5th International Conference on Biomedical Engineering in Vietnam. IFMBE Proceedings, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-11776-8_85 . ISBN:978-3-319-11775-1Santiago J, Cotto E, Jaimes LG, Vergara-Laurens, I (2017) Fall detection system for the elderly. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). Las Vegas, NV, pp 1–4. https://doi.org/10.1109/CCWC.2017.7868363Malheiros L, Nze GDA, Cardoso LX (2017) Fall detection system and body positioning with heart rate monitoring. IEEE Lat Am Trans 15(6):1021–1026Ethem Alpaydin (2010) Introduction to Machine Learning, 2nd edn. The MIT PressMezghani N, Ouakrim Y, Islam MR, Yared R, Abdulrazak B (2017) Context aware adaptable approach for fall detection bases on smart textile. 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). Orlando, FL, pp 473–476. https://doi.org/10.1109/BHI.2017.7897308Pierleoni P, Belli A, Palma L, Pellegrini M, Pernini L, Valenti S (2015) A high reliability wearable device for elderly fall detection. IEEE Sensors J 15(8):4544–4553Aziz O, Musngi M, Park EJ, Mori G, Robinovitch SN (2017) A comparison of accuracy of fall detection algorithms (threshold-based vs. machine learning) using waist-mounted tri-axial accelerometer signals from a comprehensive set of falls and non-fall trials. Med Biol Eng Comput 55(1):45–55Nguyen LP, Saleh M, Le Bouquin Jeannès R (2018) An Efficient Design of a Machine Learning-Based Elderly Fall Detector. In: Ahmed M, Begum S, Fasquel JB (eds) Internet of Things (IoT) Technologies for HealthCare. HealthyIoT 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 225. Springer, ChamÖzdemir TA, Barshan B (2014) Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6):10691–10708Tong L, Song Q, Ge Y, Liu M (2013) HMM-based human fall detection and prediction method using tri-axial accelerometer. IEEE Sensors J 13(5):1849–1856SISTEMIC: Research group on Embedded Systems and Computational Intelligence of the Electronics and Telecommunications Department at the Faculty of Engineering, University of Antioquia, “SisFall Dataset.” Online. Available: http://sistemic.udea.edu.co/investigacion/proyectos/english-falls/?lang=en . Accessed 2 Feb 2018Rubenstein L (2006) Falls in older people: epidemiology. Risk Factors and Strategies for Prev 35(Suppl 2):ii37–ii41Youn J, Okuma Y, Hwang M, Kim D, Cho JW (2017) Falling direction can predict the mechanism of recurrent falls in advanced Parkinson’s disease. Sci Rep 7(1):3921Nevitt S, Cummings MC (2018) Type of fall and risk of hip and wrist fractures: The study of osteoporotic fractures. J Am Geriatr Soc 41(11):1226–1234Karantonis DM, Narayanan MR, Mathie M, Lovell NH, Celler BG (2006) Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring. IEEE Trans Inf Technol Biomed 10(1):156–167Khan AM, Lee YK, Kim TS (2008) Accelerometer signal-based human activity recognition using augmented autoregressive model coefficients and artificial neural nets in 2008 30th Annual International. Conf Proc IEEE Eng Med Biol Soc 2008:5172–5175Yoshida T, Mizuno F, Hayasaka T, Tsubota K, Wada S, Yamaguchi T (2005) A wearable computer system for a detection and prevention of elderly users from falling. In: Proceedings of the 12th international conference on biomedical engineering. Singapore, pp 179–182Kangas M, Konttila A, Winblad I, Jamsa T (2007) Determination of simple thresholds for accelerometry based parameters for fall detection. In: 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Lyon (France), pp 1367–1370. https://doi.org/10.1109/IEMBS.2007.4352552 . E- ISSN: 1558-4615Shan S, Yuan T (2010) A wearable pre-impact fall detector using feature selection and Support Vector Machine. In: IEEE 10th International Conference on Signal Processing Proceedings. Beijin (China), pp 1686–1689. https://doi.org/10.1109/ICOSP.2010.5656840 . E- ISSN: 2164-523XLombardi A, Ferri M, Rescio G, Grassi M, Malcovati P (2009) Wearable wireless accelerometer with embedded fall-detection logic for multi-sensor ambient assisted living applications. In: 2009 IEEE Sensors. Christchurch (New Zealand), pp. 1967–1970. https://doi.org/10.1109/ICSENS.2009.5398327 . E- ISSN: 1930-0395Aziz O, Klenk J, Schwickert L, Chiari L, Becker C, Park EJ, Mori G, Robinovitch SN (2017) Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets. PLoS One 12(7):e0180318Wang K, Delbaere K, Brodie MAD, Lovell NH, Kark L, Lord SR, Redmond SJ (2017) Differences between gait on stairs and flat surfaces in relation to fall risk and future falls. IEEE J Biomed Heal Inform 21(6):1479–1486Lindholm B, Hagell P, Hansson O, Nilsson MH (2015) Prediction of falls and/or near falls in people with mild Parkinson’s disease. PLoS One 10(1):e0117018Fan Y, Levine MD, Wen G, Qiu S (2017) A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing 260:43–58Jokanovic B, Amin M, Ahmad F (2016) Radar fall motion detection using deep learning. In: 2016 IEEE Radar Conference (RadarConf). Philadelphia (USA), pp 1–6. https://doi.org/10.1109/RADAR.2016.7485147 . E- ISSN: 2375-5318Jankowski S, Szymański Z, Dziomin U, Mazurek P, Wagner J (2015) Deep learning classifier for fall detection based on IR distance sensor data. In: 2015 IEEE 8th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), vol 2. Warsar (Polonia), pp. 723–727. https://doi.org/10.1109/IDAACS.2015.7341398Jokanović B, Amin M (2018) Fall detection using deep learning in range-Doppler radars. IEEE Trans Aerosp Electron Syst 54(1):180–189Shojaei-Hashemi A, Nasiopoulos P, Little JJ, Pourazad MT (2018) Video-based Human Fall Detection in Smart Homes Using Deep Learning. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS). Florence (Italy), pp 1–5. https://doi.org/10.1109/ISCAS.2018.8351648 . E- ISSN: 2379-447XLeu F-Y, Ko C-Y, Lin Y-C, Susanto H, Yu H-C (2017) Chapter 10 - Fall Detection and Motion Classification by Using Decision Tree on Mobile Phone. In: Xhafa F, Leu F-Y, Hung L-LBT-SSN (eds) Intelligent Data-Centric Systems Book. Academic Press, pp 205–237. https://doi.org/10.1016/B978-0-12-809859-2.00013-9Yacchirema D, Suárez de Puga J, Palau C, Esteve M (2018) Fall detection system for elderly people using IoT and Big Data. In: 9th International Conference on Ambient Systems, Networks and Technologies (ANT 2018), Porto (Portugal), available at Procedia Computer Science, vol 130, pp 603–610. https://doi.org/10.1016/j.procs.2018.04.110 E-ISSN:1877-0509Rougier C, Meunier J, St-Arnaud A, Rousseau J (2011) Robust video surveillance for fall detection based on human shape deformation. IEEE Trans Circuits Syst Video Technol 21(5):611–622Stone EE, Skubic M (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Heal Inform 19(1):290–301Yuwono M, Moulton BD, Su SW, Celler BG, Nguyen HT (2012) Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems. Biomed Eng Online 11(1):9Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning, vol. 1, no. 10. Springer series in statistics New York, NY, USA. https://doi.org/10.1007/b94608 . E-ISBN: 9780387848587Zhang C, Ma Y (2012) Ensemble machine learning: Methods and applications. Springer-Verlag New York, NY. https://doi.org/10.1007/978-1-4419-9326-7 . E-ISBN 978-1-4419-9326-7Big ML (2017) Inc. US “Comprehensive Machine Learning Platform”. Online. Available: https://bigml.com/features . Accessed 12 Aug 2018Ling CX, Huang J, Zhang H et al (2003) AUC: a statistically consistent and more discriminating measure than accuracy. In: 18th Int'l Joint Conf. Artificial Intelligence (IJCAI), Acapulco (mexico), vol 3, pp 519–524. ISBN:0-7695-2728-0Dai J, Bai X, Yang Z, Shen Z, Xuan D (2010) PerFallD: A pervasive fall detection system using mobile phones. In: 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops). Mannheim (Germany), pp 292–297. https://doi.org/10.1109/PERCOMW.2010.5470652 . E- ISBN: 978-1-4244-6606-1Li Y, Ho KC, Popescu M (2012) A microphone array system for automatic fall detection. IEEE Trans Biomed Eng 59(5):1291–1301Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874Pease SG, Trueman R, Davies C, Grosberg J, Yau KH, Kaur N, Conway P, West A (2018) An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things. Futur Gener Comput Syst 79(Part 3):815–829Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140Hanke S, Mayer C, Hoeftberger O, Boos H, Wichert R, Tazari M-R, Wolf P, Furfari F (2011) universAAL -- An Open and Consolidated AAL Platform. In: Wichert R, Eberhardt B (eds) Ambient Assisted Living: 4. AAL-Kongress 2011, Berlin, Germany, January 25–26, 2011. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 127–140. https://doi.org/10.1007/978-3-642-18167-2_10 . E-ISBN: 978-3-642-18167-2Gjoreski H, Lustrek M, Gams M (2011) Accelerometer Placement for Posture Recognition and Fall Detection. In: 2011 Seventh International Conference on Intelligent Environments. Nottingham (UK), pp 47–54. doi: https://doi.org/10.1109/IE.2011.11 . E- ISBN: 978-0-7695-4452-6Parker C (2011) An Analysis of Performance Measures for Binary Classifiers. In: 2011 IEEE 11th International Conference on Data Mining, Vancouver (Canada), pp 517–526. doi: https://doi.org/10.1109/ICDM.2011.21 . E- ISSN: 2374-8486Han J, Kamber M, Pei J (2012) Data Mining Concepts and Techniques, Third Edit. Morgan Kaufmann Publishers in The Morgan Kaufmann Series in Data Management Systems. Waltham (USA). E-ISBN: 978012381480