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

  • Diana YacchiremaEmail author
  • Jara Suárez de Puga
  • Carlos Palau
  • Manuel Esteve
Original Article
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Abstract

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%.

Keywords

Fall detection Internet of Things 6LowPAN IoT gateway Ensemble learning algorithm Random Forest Accelerometer sensor Elderly people 

Notes

Funding information

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 Politécnica Nacional, Ecuador, and Secretaría de Educación Superior Ciencia, Tecnología e Innovación (SENESCYT), Ecuador.

References

  1. 1.
    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, DCGoogle Scholar
  2. 2.
    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):955–960CrossRefGoogle Scholar
  3. 3.
    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 1Google Scholar
  4. 4.
    Robie K (2010) Falls in older people: risk factors and strategies for prevention. JAMA 304(17):1958–1959CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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):812–822CrossRefGoogle Scholar
  7. 7.
    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 6:9324–9335CrossRefGoogle Scholar
  8. 8.
    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):1915–1922CrossRefGoogle Scholar
  9. 9.
    Yang L, Ren Y, Zhang W (2016) 3D depth image analysis for indoor fall detection of elderly people. Digit Commun Netw 2(1):24–34CrossRefGoogle Scholar
  10. 10.
    Mastorakis G, Makris D (2014) Fall detection system using Kinect’s infrared sensor. J Real-Time Image Process 9(4):635–646CrossRefGoogle Scholar
  11. 11.
    Kwolek B, Kepski M (2014) Human fall detection on embedded platform using depth maps and wireless accelerometer. Comput Methods Prog Biomed 117(3):489–501CrossRefGoogle Scholar
  12. 12.
    Wang Y, Wu K, Ni LM (2017) WiFall: device-free fall detection by wireless networks. IEEE Trans Mob Comput 16(2):581–594CrossRefGoogle Scholar
  13. 13.
    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
  14. 14.
    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 34:3–13CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Kau L, Chen C (2015) A smart phone-based pocket fall accident detection, positioning, and rescue system. IEEE J Biomed Heal Inform 19(1):44–56CrossRefGoogle Scholar
  18. 18.
    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 CrossRefGoogle Scholar
  19. 19.
    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 CrossRefGoogle Scholar
  20. 20.
    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 CrossRefGoogle Scholar
  21. 21.
    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):e0140929CrossRefGoogle Scholar
  22. 22.
    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
  23. 23.
    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
  24. 24.
    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
  25. 25.
    Malheiros L, Nze GDA, Cardoso LX (2017) Fall detection system and body positioning with heart rate monitoring. IEEE Lat Am Trans 15(6):1021–1026CrossRefGoogle Scholar
  26. 26.
    Ethem Alpaydin (2010) Introduction to Machine Learning, 2nd edn. The MIT PressGoogle Scholar
  27. 27.
    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
  28. 28.
    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):4544–4553CrossRefGoogle Scholar
  29. 29.
    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):45–55CrossRefGoogle Scholar
  30. 30.
    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, ChamGoogle Scholar
  31. 31.
    Özdemir TA, Barshan B (2014) Detecting falls with wearable sensors using machine learning techniques. Sensors 14(6):10691–10708CrossRefGoogle Scholar
  32. 32.
    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):1849–1856CrossRefGoogle Scholar
  33. 33.
    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
  34. 34.
    Rubenstein L (2006) Falls in older people: epidemiology. Risk Factors and Strategies for Prev 35(Suppl 2):ii37–ii41Google Scholar
  35. 35.
    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):3921CrossRefGoogle Scholar
  36. 36.
    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):1226–1234CrossRefGoogle Scholar
  37. 37.
    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):156–167CrossRefGoogle Scholar
  38. 38.
    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 2008:5172–5175Google Scholar
  39. 39.
    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 179–182Google Scholar
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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
  43. 43.
    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):e0180318CrossRefGoogle Scholar
  44. 44.
    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):1479–1486CrossRefGoogle Scholar
  45. 45.
    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):e0117018CrossRefGoogle Scholar
  46. 46.
    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 260:43–58CrossRefGoogle Scholar
  47. 47.
    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
  48. 48.
    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
  49. 49.
    Jokanović B, Amin M (2018) Fall detection using deep learning in range-Doppler radars. IEEE Trans Aerosp Electron Syst 54(1):180–189CrossRefGoogle Scholar
  50. 50.
    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
  51. 51.
    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
  52. 52.
    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
  53. 53.
    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):611–622CrossRefGoogle Scholar
  54. 54.
    Stone EE, Skubic M (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE J Biomed Heal Inform 19(1):290–301CrossRefGoogle Scholar
  55. 55.
    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):9CrossRefGoogle Scholar
  56. 56.
    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
  57. 57.
    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
  58. 58.
    Big ML (2017) Inc. US “Comprehensive Machine Learning Platform”. Online. Available: https://bigml.com/features. Accessed 12 Aug 2018
  59. 59.
    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:0-7695-2728-0Google Scholar
  60. 60.
    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
  61. 61.
    Li Y, Ho KC, Popescu M (2012) A microphone array system for automatic fall detection. IEEE Trans Biomed Eng 59(5):1291–1301CrossRefGoogle Scholar
  62. 62.
    Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27(8):861–874MathSciNetCrossRefGoogle Scholar
  63. 63.
    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):815–829CrossRefGoogle Scholar
  64. 64.
    Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140zbMATHGoogle Scholar
  65. 65.
    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
  66. 66.
    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
  67. 67.
    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
  68. 68.
    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: 9780123814807Google Scholar

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Authors and Affiliations

  1. 1.Escuela Politécnica NacionalQuitoEcuador
  2. 2.Universitat Politècnica de ValènciaValenciaSpain

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