Monitoring Activities of Daily Living Using Audio Analysis and a RaspberryPI: A Use Case on Bathroom Activity Monitoring

  • Georgios Siantikos
  • Theodoros Giannakopoulos
  • Stasinos Konstantopoulos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 736)


A framework that utilizes audio information for recognition of activities of daily living (ADLs) in the context of a health monitoring environment is presented in this chapter. We propose integrating a Raspberry PI single-board PC that is used both as an audio acquisition and analysis unit. So Raspberry PI captures audio samples from the attached microphone device and executes a set of real-time feature extraction and classification procedures, in order to provide continuous and online audio event recognition to the end user. Furthermore, a practical workflow is presented, that helps the technicians that setup the device to perform a fast, user-friendly and robust tuning and calibration procedure. As a result, the technician is capable of “training” the device without any need for prior knowledge of machine learning techniques. The proposed system has been evaluated against a particular scenario that is rather important in the context of any healthcare monitoring system for the elder: In particular, we have focused on the “bathroom scenario” according to which, a Raspberry PI device equipped with a single microphone is used to monitor bathroom activity on a 24/7 basis in a privacy-aware manner, since no audio data is stored or transmitted. The presented experimental results prove that the proposed framework can be successfully used for audio event recognition tasks.


Audio analysis Activities of daily living Health monitoring Remote monitoring Audio sensors RaspberryPI Audio event recognition 



This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 643892. Please see for more details.


  1. 1.
    Barger, T.S., Brown, D.E., Alwan, M.: Health-status monitoring through analysis of behavioral patterns. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 35, 22–27 (2005)CrossRefGoogle Scholar
  2. 2.
    Hagler, S., Austin, D., Hayes, T.L., Kaye, J., Pavel, M.: Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. IEEE Trans. Biomed. Eng. 57, 813–820 (2010)CrossRefGoogle Scholar
  3. 3.
    Mann, W.C., Marchant, T., Tomita, M., Fraas, L., Stanton, K.: Elder acceptance of health monitoring devices in the home. Care Manage. J. 3, 91 (2002)Google Scholar
  4. 4.
    Vacher, M., Portet, F., Fleury, A., Noury, N.: Development of audio sensing technology for ambient assisted living: applications and challenges. In: Digital Advances in Medicine, E-Health, and Communication Technologies, p. 148 (2013)Google Scholar
  5. 5.
    Vacher, M., Portet, F., Fleury, A., Noury, N.: Challenges in the processing of audio channels for ambient assisted living. In: 12th IEEE International Conference on e-Health Networking Applications and Services (Healthcom), pp. 330–337. IEEE (2010)Google Scholar
  6. 6.
    Costa, R., Carneiro, D., Novais, P., Lima, L., Machado, J., Marques, A., Neves, J.: Ambient assisted living. Ubiquitous Computing and Ambient Intelligence, pp. 86–94. Springer, Heidelberg (2009)Google Scholar
  7. 7.
    Botia, J.A., Villa, A., Palma, J.: Ambient assisted living system for in-home monitoring of healthy independent elders. Expert Syst. Appl. 39, 8136–8148 (2012)CrossRefGoogle Scholar
  8. 8.
    Siantikos, G., Giannakopoulos, T., Konstantopoulos, S.: A low-cost approach for detecting activities of daily living using audio information: a use case on bathroom activity monitoring. In: Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health, pp. 26–32 (2016)Google Scholar
  9. 9.
    Giannakopoulos, T., Pikrakis, A.: Introduction to Audio Analysis: A MATLAB® Approach. Academic Press (2014)Google Scholar
  10. 10.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press, Inc. (2008)Google Scholar
  11. 11.
    Hyoung-Gook, K., Nicolas, M., Sikora, T.: MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval. Wiley, Chichester (2005)Google Scholar
  12. 12.
    Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in large margin classifiers, Citeseer (1999)Google Scholar
  13. 13.
    Chapelle, O., Haffner, P., Vapnik, V.N.: Support vector machines for histogram-based image classification. IEEE Trans. Neural Networks 10, 1055–1064 (1999)CrossRefGoogle Scholar
  14. 14.
    Giannakopoulos, T.: pyAudioAnalysis: Python audio analysis library: feature extraction, classification, segmentation and applications (2015). Accessed 27 Apr 2015Google Scholar
  15. 15.
    RADIO Project: D2.2: Early detection methods and relevant system requirements (2015).
  16. 16.
    Chen, J., Kam, A.H., Zhang, J., Liu, N., Shue, L.: Bathroom activity monitoring based on sound. In: Gellersen, H.-W., Want, R., Schmidt, A. (eds.) Pervasive 2005. LNCS, vol. 3468, pp. 47–61. Springer, Heidelberg (2005). doi: 10.1007/11428572_4 CrossRefGoogle Scholar
  17. 17.
    Vuegen, L., Van Den Broeck, B., Karsmakers, P., Vanrumste, B., et al.: Automatic monitoring of activities of daily living based on real-life acoustic sensor data: a preliminary study. In: Fourth workshop on speech and language processing for assistive technologies (SLPAT): Proceedings, Association for Computational Linguistics (ACL), pp. 113–118 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Georgios Siantikos
    • 1
  • Theodoros Giannakopoulos
    • 1
  • Stasinos Konstantopoulos
    • 1
  1. 1.Institute of Informatics and Telecommunications, NCSR DemokritosAthensGreece

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