Big Data Analytics in Smart Living Environments for Elderly Monitoring

  • Giovanni DiracoEmail author
  • Alessandro Leone
  • Pietro Siciliano
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)


Today, data collected in smart-living environments are constantly increasing in the dimensions of volume, velocity and variety, which characterize any big data application. In such a way, it makes sense to investigate big data analytics for elderly monitoring at home. The aim of this study is to conduct a preliminary investigation of state-of-the-art algorithms for abnormal activity detection and change prediction, suitable to deal with big data. The algorithmic approaches, under evaluation and comparison, belong to the three main categories of supervised, semi-supervised and unsupervised techniques. At this purpose, specific synthetic data are generated, including activities of daily living, home locations in which such activities take place, as well as physiological parameters. All techniques are evaluated in terms of abnormality-detection accuracy and lead-time of prediction, using the generated datasets with various kinds of perturbation. The achieved results, even though preliminary, are very encouraging, showing that unsupervised deep-learning techniques outperform traditional (machine learning) ones, with detection accuracy greater than 96% and prediction lead-time of about 15 days in advance.


Smart living Elderly monitoring Abnormal activity Detection change prediction Big data analytics Machine learning Deep learning 


  1. 1.
    Gokalp H, Clarke M (2013) Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: a review. Telemed e-Health 19(12):910–923CrossRefGoogle Scholar
  2. 2.
    Sharma R, Nah F, Sharma K, Katta T, Pang N, Yong A (2016) Smart living for elderly: design and human-computer interaction considerations. Lect Notes Comput Sci 9755:112–122CrossRefGoogle Scholar
  3. 3.
    Parisa R, Mihailidis A (2013) A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inform 17(3):579–590CrossRefGoogle Scholar
  4. 4.
    Vimarlund V, Wass S (2014) Big data, smart homes and ambient assisted living. Yearb Med Inform 9(1):143–149Google Scholar
  5. 5.
    Mabrouk AB, Zagrouba E (2018) Abnormal behavior recognition for intelligent video surveillance systems: a review. Expert Syst Appl 91:480–491CrossRefGoogle Scholar
  6. 6.
    Bakar U, Ghayvat H, Hasanm SF, Mukhopadhyay SC (2015) Activity and anomaly detection in smart home: a survey. Next Gener Sensors Syst 16:191–220CrossRefGoogle Scholar
  7. 7.
    Taraldsen K, Chastin SFM, Riphagen II, Vereijken B, Helbostad JL (2012) Physical activity monitoring by use of accelerometer-based body-worn sensors in older adults: a systematic literature review of current knowledge and applications. Maturitas 71(1):13–19CrossRefGoogle Scholar
  8. 8.
    Min C, Kang S, Yoo C, Cha J, Choi S, Oh Y, Song J (Sept 2015) Exploring current practices for battery use and management of smartwatches. In: Proceedings of: the 2015 ACM international symposium on wearable computers, pp 11–18Google Scholar
  9. 9.
    Droghini D, Ferretti D, Principi E, Squartini S, Piazza F (2017) A combined one-class SVM and template-matching approach for user-aided human fall detection by means of floor acoustic features. Comput Intell NeurosciGoogle Scholar
  10. 10.
    Hussmann S, Ringbeck T, Hagebeuker B (2008) A performance review of 3D TOF vision systems in comparison to stereo vision systems. In: Stereo vision, InTechGoogle Scholar
  11. 11.
    Diraco G, Leone A, Siciliano P (2017) A radar-based smart sensor for unobtrusive elderly monitoring in ambient assisted living applications. Biosensors 7(4):55Google Scholar
  12. 12.
    Caroppo A, Diraco G, Rescio G, Leone A, Siciliano P (2015) Heterogeneous sensor platform for circadian rhythm analysis. In: IEEE international workshop on advances in sensors and interfaces (ISIE), 10 August 2015, pp 187–192Google Scholar
  13. 13.
    Diraco G, Leone A, Siciliano P Geodesic-based human posture analysis by using a single 3D TOF camera. In: IEEE international symposium on industrial electronics (ISIE), 27–30 June 2011Google Scholar
  14. 14.
    Diraco G, Leone A, Siciliano P (2014) In-home hierarchical posture classification with a time-of-flight 3D sensor. Gait Posture 39(1):182–187CrossRefGoogle Scholar
  15. 15.
    Miao Y, Song J (2014) Abnormal event detection based on SVM in video surveillance. In: IEEE workshop on advance research and technology in industry applications, pp 1379–1383Google Scholar
  16. 16.
    Forkan ARM, Khalil I, Tari Z, Foufou S, Bouras A (2015) A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recogn 48(3):628–641CrossRefGoogle Scholar
  17. 17.
    Hejazi M, Singh YP (2013) One-class support vector machines approach to anomaly detection. Appl Artif Intell 27(5):351–366CrossRefGoogle Scholar
  18. 18.
    Otte FJP, Rosales Saurer B, Stork W (2013) Unsupervised learning in ambient assisted living for pattern and anomaly detection: a survey. In: Communications in computer and information science 413 CCIS, pp 44–53Google Scholar
  19. 19.
    Sohangir S, Wang D, Pomeranets A, Khoshgoftaar TM (2018) Big data: deep learning for financial sentiment analysis. J Big Data 5(1):3Google Scholar
  20. 20.
    Ribeiro M, Lazzaretti AE, Lopes HS (2018) A study of deep convolutional auto-encoders for anomaly detection in videos. Pattern Recogn Lett 105:13–22CrossRefGoogle Scholar
  21. 21.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105Google Scholar
  22. 22.
    Krizhevsky A, Hinton GE (2011) Using very deep autoencoders for content-based image retrieval. In: ESANN, April 2011Google Scholar
  23. 23.
    Masci J, Meier U, Cireşan D, Schmidhuber J (2011) Stacked convolutional auto-encoders for hierarchical feature extraction. In: International conference on artificial neural networks, Springer, Berlin, Heidelberg, pp 52–59Google Scholar
  24. 24.
    Guo X, Liu X, Zhu E, Yin J (2017) Deep clustering with convolutional autoencoders. In: International conference on neural information processing, Springer, pp 373–382, November 2017Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Giovanni Diraco
    • 1
    Email author
  • Alessandro Leone
    • 1
  • Pietro Siciliano
    • 1
  1. 1.National Research Council of Italy, Institute for Microelectronics and MicrosystemsLecceItaly

Personalised recommendations