A Low-cost Approach for Detecting Activities of Daily Living using Audio Information: A Use Case on Bathroom Activity Monitoring

Georgios Siantikos, Theodoros Giannakopoulos, Stasinos Konstantopoulos

Abstract

In this paper, we present an architecture for recognizing events related to activities of daily living in the context of a health monitoring environment. The proposed approach explores the integration of a Raspberry PI singleboard PC both as an audio acquisition and analysis unit. A set of real-time feature extraction and classification procedures has been implemented and integrated on the Raspberry PI device, in order to provide continuous and online audio event recognition. In addition, a tuning and calibration workflow is presented, according to which the technicians installing the device in a fast ans user-friendly manner, without any requirements for machine learning expertise. The proposed approach has been evaluated against a particular scenario that is rather important in the context of any healthcare monitoring system for the elder, namely the ”bathroom scenario” according to which a single microphone installed on a Raspberry PI device is used to monitor bathroom activity in a 24/7 basis. Experimental results indicate a satisfactory performance rate on the classification process (around 70% for five bathroom-related audio classes) even when less than two minutes of annotated data are used for training in the installation procedure. This makes the whole procedure non demanding in terms of time and effort needed to be calibrated by the technician.

References

  1. Barger, T. S., Brown, D. E., and Alwan, M. (2005). Healthstatus monitoring through analysis of behavioral patterns. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 35(1):22-27.
  2. Botia, J. A., Villa, A., and Palma, J. (2012). Ambient assisted living system for in-home monitoring of healthy independent elders. Expert Systems with Applications, 39(9):8136-8148.
  3. Chapelle, O., Haffner, P., and Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. Neural Networks, IEEE Transactions on, 10(5):1055-1064.
  4. Chen, J., Kam, A. H., Zhang, J., Liu, N., and Shue, L. (2005). Bathroom activity monitoring based on sound. In Pervasive Computing, pages 47-61. Springer.
  5. Costa, R., Carneiro, D., Novais, P., Lima, L., Machado, J., Marques, A., and Neves, J. (2009). Ambient assisted living. In 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008, pages 86-94. Springer.
  6. Giannakopoulos, T. (2015-). pyAudioAnalysis: Python audio analysis library: Feature extraction, classification, segmentation and applications. [Online; accessed 2015-04-27].
  7. Giannakopoulos, T. and Pikrakis, A. (2014). Introduction to Audio Analysis: A MATLAB R Approach. Academic Press.
  8. Hagler, S., Austin, D., Hayes, T. L., Kaye, J., and Pavel, M. (2010). Unobtrusive and ubiquitous in-home monitoring: a methodology for continuous assessment of gait velocity in elders. Biomedical Engineering, IEEE Transactions on, 57(4):813-820.
  9. Hyoung-Gook, K., Nicolas, M., and Sikora, T. (2005). MPEG-7 Audio and Beyond: Audio Content Indexing and Retrieval. John Wiley & Sons.
  10. Lane, N. D., Georgiev, P., and Qendro, L. (2015). Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pages 283-294. ACM.
  11. MOTS, T. M., Linda Fraas OTR, M., and Kathleen Stanton MS, R. (2002). Elder acceptance of health monitoring devices in the home. Care Management Journals, 3(2):91.
  12. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in large margin classifiers . Citeseer.
  13. RADIO Project (2015). D2.2: Early detection methods and relevant system requirements. Available at http://radio-project.eu/deliverables.
  14. Theodoridis, S. and Koutroumbas, K. (2008). Pattern Recognition, Fourth Edition. Academic Press, Inc.
  15. Vacher, M., Portet, F., Fleury, A., and Noury, N. (2010). Challenges in the processing of audio channels for ambient assisted living. In e-Health Networking Applications and Services (Healthcom), 2010 12th IEEE International Conference on, pages 330-337. IEEE.
  16. Vacher, M., Portet, F., Fleury, A., and Noury, N. (2013). Development of audio sensing technology for ambient assisted living: Applications and challenges. Digital Advances in Medicine, E-Health, and Communication Technologies, page 148.
  17. Vuegen, L., Van Den Broeck, B., Karsmakers, P., Vanrumste, B., et al. (2013). 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, pages 113-118. Association for Computational Linguistics (ACL).
  18. Wang, H.-H., Liu, J.-M., You, M., and Li, G.-Z. (2015). Audio signals encoding for cough classification using convolutional neural networks: A comparative study. In Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on, pages 442- 445. IEEE.
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Paper Citation


in Harvard Style

Siantikos G., Giannakopoulos T. and Konstantopoulos S. (2016). 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 - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016) ISBN 978-989-758-180-9, pages 26-32. DOI: 10.5220/0005803700260032


in Bibtex Style

@conference{ict4awe16,
author={Georgios Siantikos and Theodoros Giannakopoulos and Stasinos Konstantopoulos},
title={A Low-cost Approach for Detecting Activities of Daily Living using Audio Information: A Use Case on Bathroom Activity Monitoring},
booktitle={Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)},
year={2016},
pages={26-32},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005803700260032},
isbn={978-989-758-180-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Information and Communication Technologies for Ageing Well and e-Health - Volume 1: ICT4AWE, (ICT4AGEINGWELL 2016)
TI - A Low-cost Approach for Detecting Activities of Daily Living using Audio Information: A Use Case on Bathroom Activity Monitoring
SN - 978-989-758-180-9
AU - Siantikos G.
AU - Giannakopoulos T.
AU - Konstantopoulos S.
PY - 2016
SP - 26
EP - 32
DO - 10.5220/0005803700260032