- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

[Cerrado]
Nombre: Yacchirema;Suárez ...
Tamaño: 2.576Mb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Yacchirema, Diana es_ES
dc.contributor.author Suárez de Puga, Jara es_ES
dc.contributor.author Palau Salvador, Carlos Enrique es_ES
dc.contributor.author Esteve Domingo, Manuel es_ES
dc.date.accessioned 2021-02-19T04:34:34Z
dc.date.available 2021-02-19T04:34:34Z
dc.date.issued 2019-11 es_ES
dc.identifier.issn 1617-4909 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161870
dc.description.abstract [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%. es_ES
dc.description.sponsorship 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. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Personal and Ubiquitous Computing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Fall detection es_ES
dc.subject Internet of Things es_ES
dc.subject 6LowPAN es_ES
dc.subject IoT gateway es_ES
dc.subject Ensemble learning algorithm es_ES
dc.subject Random Forest es_ES
dc.subject Accelerometer sensor es_ES
dc.subject Elderly people es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Fall detection system for elderly people using IoT and ensemble machine learning algorithm es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s00779-018-01196-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/687283/EU/Interoperability of Heterogeneous IoT Platforms/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation 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/s00779-018-01196-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s00779-018-01196-8 es_ES
dc.description.upvformatpinicio 801 es_ES
dc.description.upvformatpfin 817 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 23 es_ES
dc.description.issue 5-6 es_ES
dc.relation.pasarela S\381147 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Escuela Politécnica Nacional, Ecuador es_ES
dc.contributor.funder Secretaría de Educación Superior, Ciencia, Tecnología e Innovación, Ecuador es_ES
dc.description.references He 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, DC es_ES
dc.description.references Bousquet 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):955–960 es_ES
dc.description.references Yacchirema 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 1 es_ES
dc.description.references Robie K (2010) Falls in older people: risk factors and strategies for prevention. JAMA 304(17):1958–1959 es_ES
dc.description.references Jrad 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.2014.6861053 es_ES
dc.description.references Chaccour 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):812–822 es_ES
dc.description.references Min 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 6:9324–9335 es_ES
dc.description.references Ma 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):1915–1922 es_ES
dc.description.references Yang L, Ren Y, Zhang W (2016) 3D depth image analysis for indoor fall detection of elderly people. Digit Commun Netw 2(1):24–34 es_ES
dc.description.references Mastorakis G, Makris D (2014) Fall detection system using Kinect’s infrared sensor. J Real-Time Image Process 9(4):635–646 es_ES
dc.description.references Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Prog Biomed 117(3):489–501 es_ES
dc.description.references Wang Y, Wu K, Ni LM (2017) WiFall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594 es_ES
dc.description.references Sehairi 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.2018.8354084 es_ES
dc.description.references Álvarez 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 34:3–13 es_ES
dc.description.references Fortino 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.28-9-2015.2261462 es_ES
dc.description.references Aguiar 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.2014.6860110 es_ES
dc.description.references Kau L, Chen C (2015) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Heal Inform 19(1):44–56 es_ES
dc.description.references He 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/s17061393 es_ES
dc.description.references Santoyo-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/s18041155 es_ES
dc.description.references Mao 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/s17092096 es_ES
dc.description.references Casilari E, Oviedo-Jiménez MA (2015) Automatic fall detection system based on the combined use of a smartphone and a smartwatch. PLoS One 10(11):e0140929 es_ES
dc.description.references Dias 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.2016.7811576 es_ES
dc.description.references Phu 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:978-3-319-11775-1 es_ES
dc.description.references Santiago 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.2017.7868363 es_ES
dc.description.references Malheiros L, Nze GDA, Cardoso LX (2017) Fall detection system and body positioning with heart rate monitoring. IEEE Lat Am Trans 15(6):1021–1026 es_ES
dc.description.references Ethem Alpaydin (2010) Introduction to Machine Learning, 2nd edn. The MIT Press es_ES
dc.description.references Mezghani 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.2017.7897308 es_ES
dc.description.references Pierleoni 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):4544–4553 es_ES
dc.description.references Aziz 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):45–55 es_ES
dc.description.references Nguyen 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, Cham es_ES
dc.description.references Özdemir TA, Barshan B (2014) Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6):10691–10708 es_ES
dc.description.references Tong 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):1849–1856 es_ES
dc.description.references SISTEMIC: 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 2018 es_ES
dc.description.references Rubenstein L (2006) Falls in older people: epidemiology. Risk Factors and Strategies for Prev 35(Suppl 2):ii37–ii41 es_ES
dc.description.references Youn 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):3921 es_ES
dc.description.references Nevitt 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):1226–1234 es_ES
dc.description.references Karantonis 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):156–167 es_ES
dc.description.references Khan 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 2008:5172–5175 es_ES
dc.description.references Yoshida 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 179–182 es_ES
dc.description.references Kangas 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: 1558-4615 es_ES
dc.description.references Shan 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: 2164-523X es_ES
dc.description.references Lombardi 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: 1930-0395 es_ES
dc.description.references Aziz 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):e0180318 es_ES
dc.description.references Wang 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):1479–1486 es_ES
dc.description.references Lindholm 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):e0117018 es_ES
dc.description.references Fan Y, Levine MD, Wen G, Qiu S (2017) A deep neural network for real-time detection of falling humans in naturally occurring scenes. Neurocomputing 260:43–58 es_ES
dc.description.references Jokanovic 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: 2375-5318 es_ES
dc.description.references Jankowski 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.2015.7341398 es_ES
dc.description.references Jokanović B, Amin M (2018) Fall detection using deep learning in range-Doppler radars. IEEE Trans Aerosp Electron Syst 54(1):180–189 es_ES
dc.description.references Shojaei-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: 2379-447X es_ES
dc.description.references Leu 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/B978-0-12-809859-2.00013-9 es_ES
dc.description.references Yacchirema 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:1877-0509 es_ES
dc.description.references Rougier 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):611–622 es_ES
dc.description.references Stone EE, Skubic M (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Heal Inform 19(1):290–301 es_ES
dc.description.references Yuwono 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):9 es_ES
dc.description.references Friedman 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: 9780387848587 es_ES
dc.description.references Zhang 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 978-1-4419-9326-7 es_ES
dc.description.references Big ML (2017) Inc. US “Comprehensive Machine Learning Platform”. Online. Available: https://bigml.com/features . Accessed 12 Aug 2018 es_ES
dc.description.references Ling 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:0-7695-2728-0 es_ES
dc.description.references Dai 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: 978-1-4244-6606-1 es_ES
dc.description.references Li Y, Ho KC, Popescu M (2012) A microphone array system for automatic fall detection. IEEE Trans Biomed Eng 59(5):1291–1301 es_ES
dc.description.references Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874 es_ES
dc.description.references Pease 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):815–829 es_ES
dc.description.references Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140 es_ES
dc.description.references Hanke 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: 978-3-642-18167-2 es_ES
dc.description.references Gjoreski 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: 978-0-7695-4452-6 es_ES
dc.description.references Parker 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: 2374-8486 es_ES
dc.description.references Han 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: 9780123814807 es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem