Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis

Bingchuan Yuan, John Herbert, Yalda Emamian

2014

Abstract

Learning and recognizing the activities of daily living (ADLs) of an individual is vital when providing an individual with context-aware at-home healthcare. In this work, unobtrusive detection of inhabitants’ activities in the home environment is implemented through the smartphone and wearable wireless sensor belt solution. A hybrid classifier is developed by combining threshold-based methods and machine learning mechanisms. Features extracted from the raw inertial sensor data are collected from a Body Area Network (BAN) (consisting of the Zephyr BioHarness sensor and an Android smartphone), and are used to build classification models using different machine learning algorithms. A cloud-based data analytics framework is developed to process different classification models in parallel and to select the most suitable model for each user. The evolving machine learning mechanism makes the model become customizable and self-adaptive by utilizing a cloud infrastructure which also overcomes the limitation of the computing power and storage of a smartphone. Furthermore, we investigate methods for adapting a universal model, which is trained using the data set of all users, to an individual user through an unsupervised learning scheme. The evaluation results of the experiments conducted on eight participants indicate that the proposed approach can robustly identify activities in real-time across multiple individuals: the highest recognition rate achieved 98% after a few runs.

References

  1. Andreu, J. and Angelov, P. (2013). An evolving machine learning method for human activity recognition systems. Journal of Ambient Intelligence and Humanized Computing.
  2. Bao, L. and Intille, S. (2004). Activity recognition from user-annotated acceleration data. Pervasive Computing.
  3. Choudhury, T. and Consolvo, S. (2008). The mobile sensing platform: An embedded activity recognition system. IEEE2008 Pervasive Computing.
  4. Frederking, R. and Brown, R. (2010). The pangloss-lite machine translation system. In Proceedings of the Second Conference of the Association for Machine Translation in the Americas.
  5. Huerta-Canepa, G. and Lee, D. (2010). A virtual cloud computing provider for mobile devices. In Proceeding of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond.
  6. J.R. Kwapisz, G. W. and Moore, S. (2010). Activity recognition using cell phone accelerometers. In Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data.
  7. Khan, A., Lee, Y., Lee, S., and Kim, T. (2010). Human activity recognition via an accelerometer-enabledsmartphone using kernel discriminant analysis. In 5th International Conference on Future Information Technology.
  8. Lyons, G., Culhane, K., Hilton, D., Grace, P., and Lyons, D. (2005). A description of an accelerometer-based mobility monitoring technique. Medical Engineering & Physics.
  9. Marinelli, E. (2009). Hyrax: cloud computing on mobile devices using mapreduce. Master's thesis, Carnegie Mellon University.
  10. Maurer, U., Samilagic, A., Siewiorek, D., and Deisher, M. (2006). Activity recognition and monitoring using multiple sensors on different body positions. In IEEE Proceedings on the International Workshop on Wearable and Implantable Sensor Networks.
  11. Ravi, N., Dandekar, N., Mysore, P., and Littman, M. L. (2005). Activity recognition from accelerometer data. In Proceeding of the National Conference on Artificial Intelligence.
  12. Samsung Inc. (2012). Android smartphone: Samsung galaxy siii. Description Available online at http://en.wikipedia.org/wiki/Samsung Galaxy S III.
  13. Singer, E. (2011). The measured life. In Technology Review, volume 114.
  14. Skelton, D. and McLaughlin, A. (1996). Train functional ability in old age. Physiotherapy.
  15. Stefan, D., Das, B., Krishnan, N., Thomas, B., and Cook, D. (2012). Simple and complex activity recognition through smart phones. In 8th International Conference on Intelligent Environments.
  16. Tapia, E., Intille, S., Haskell, W., Larson, K., and Friedman, R. (2007). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. In Proceedings of the 11th IEEE International Symposium on Wearable Computers & Physics.
  17. Tapia, E., Intille, S., and Larson, K. (2004). Activity recognition in the home setting using simple and ubiquitous sensors. In Proceedings of PERVASIVE.
  18. White, D., Wagenaar, R., and Ellis, T. (2001). Monitoring activity in individuals with parkinson's disease: A validity study. Jurnal of Neurological Physical Therapy, 30.
  19. Witten, H., Frank, E., and Hall, A. (2011). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco, 3rd edition edition.
  20. Yuan, B. and Herbert, J. (2011a). Non-intrusive movement detection in cara pervasive healthcare application. In The 2011 International Conference on Wireless Networks.
  21. Yuan, B. and Herbert, J. (2011b). Web-based real-time remote monitoring for pervasive healthcare. In IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).
  22. Yuan, B. and Herbert, J. (2012). Fuzzy cara - a fuzzy-based context reasoning system for pervasive healthcare. In Procedia Computer Science, volume 10.
  23. Zephyr Inc. (2011). Wireless professional heart rate monitor & physiological monitor with bluetooth. Description Available online at http://www.zephyrtechnology.com/products/bioharness-3/.
  24. Zhang, S., McCullagh, P., Nugent, C., and Zheng, H. (2010). Activity monitoring using a smart phone's accelerometer with hierarchical classification. In 6th International Conference on Intelligent Environments.
Download


Paper Citation


in Harvard Style

Yuan B., Herbert J. and Emamian Y. (2014). Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis . In Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS, ISBN 978-989-758-000-0, pages 14-23. DOI: 10.5220/0004723900140023


in Bibtex Style

@conference{peccs14,
author={Bingchuan Yuan and John Herbert and Yalda Emamian},
title={Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis},
booktitle={Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,},
year={2014},
pages={14-23},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004723900140023},
isbn={978-989-758-000-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Pervasive and Embedded Computing and Communication Systems - Volume 1: PECCS,
TI - Smartphone-based Activity Recognition using Hybrid Classifier - Utilizing Cloud Infrastructure for Data Analysis
SN - 978-989-758-000-0
AU - Yuan B.
AU - Herbert J.
AU - Emamian Y.
PY - 2014
SP - 14
EP - 23
DO - 10.5220/0004723900140023