Skip to main content
SpringerLink
Log in

Ensemble Learning-Based Algorithms for Aggressive and Agitated Behavior Recognition

  • Conference paper
  • First Online:
Book cover Ubiquitous Computing and Ambient Intelligence (IWAAL 2016, AmIHEALTH 2016, UCAmI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10070))

Abstract

This paper addresses a practical and challenging problem concerning the recognition of behavioral symptoms dementia (BSD) such as aggressive and agitated behaviors. We propose two new algorithms for the recognition of these behaviors using two different sensors such as a Microsoft Kinect and an Accelerometer sensor. The first algorithm extracts skeleton based features from 3D joint positions data collected by a Kinect sensor, while the second algorithm extracts features from acceleration data collected by a Shimmer accelerometer sensor. Classification is then performed in both algorithms using ensemble learning classifier. We compared the performance of both algorithms in terms of recognition accuracy and processing time. The results obtained, through extensive experiments on a real dataset, showed better performance of the Accelerometer-based algorithm over the Kinect-based algorithm in terms of processing time, and less performance in terms of recognition accuracy. The results also showed how our algorithms outperformed several state of the art methods.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.microsoft.com/en-us/kinectforwindows/purchase/.

  2. 2.

    www.shimmersensing.com.

  3. 3.

    Here we use the terms Behavior and Action interchangeably.

  4. 4.

    http://www.shimmersensing.com/shop/shimmer3.

References

  1. Mihailidis, A., Boger, J., Craig, T., Hoey, J.: The coach prompting system to assist older adults with dementia through handwashing: an efficacy study. BMC Geriatr. 8(1), 28 (2008)

    Article  Google Scholar 

  2. Desai, A., Grossberg, G.: Recognition and management of behavioral disturbances in dementia. Prim. Care Companion J. Clin. Psychiatry 3(3), 93 (2001)

    Article  Google Scholar 

  3. Qiu, Q., Foo, S., Wai, A., Pham, V., Maniyeri, J., Biswas, J., Yap, P.: Multimodal information fusion for automated recognition of complex agitation behaviors of dementia patients. In: 2007 10th International Conference on Information Fusion, pp. 1–8. IEEE (2007)

    Google Scholar 

  4. Foo, S., Pham, V., Htwe, T., Qiu, Q., Wai, A., Maniyeri, J., Biswas, J., Yap, P.: Automated recognition of complex agitation behavior of demented patient using video camera. In: Conference One-Health Networking, Application and Services, pp. 68–73 (2007)

    Google Scholar 

  5. Wouter, V., Egon, V., Reinier, K., Schavemaker, J.: Towards sensing behavior using the kinect. In: 8th International Conference on Methods and Techniques in Behavioural Research, pp. 372–375. Noldus Information Technology (2012)

    Google Scholar 

  6. Banos, O., Calatroni, A., Damas, M., Pomares, H., Rojas, I., Sagha, H., Milln, J., Troster, G., Chavarriaga, R., Roggen, D.: Kinect=imu? learning mimo signal mappings to automatically translate activity recognition systems across sensor modalities. In: ISWC, pp. 92–99 (2012)

    Google Scholar 

  7. Nishkam, R., Nikhil, D., Preetham, M., Littman, M.: Activity recognition from accelerometer data. AAAI 5, 1541–1546 (2005)

    Google Scholar 

  8. Yun, X., Bachmann, E., Moore, H., Calusdian, J.: Self-contained position tracking of human movement using small inertial/magnetic sensor modules. In: IEEE International Conference on Robotics and Automation, pp. 2526–2533. IEEE (2007)

    Google Scholar 

  9. Xia, L., Chen, C., Aggarwal, J.: View invariant human action recognition using histograms of 3d joints. In: CVPR Workshops, pp. 20–27 (2012)

    Google Scholar 

  10. Chen, C., Jafari, R., Kehtarnavaz, N.: A survey of depth and inertial sensor fusion for human action recognition. Multimed. Tools Appl., 1–21 (2015)

    Google Scholar 

  11. Nirjon, S., Greenwood, C., Torres, C., Zhou, S., Stankovic, J., Yoon, H., Ra, H., Basaran, C., Park, T., Son, S. Kintense: a robust, accurate, real-time and evolving system for detecting aggressive actions from streaming 3d skeleton data. In: ACM Conference on Embedded Networked Sensor Systems, pp. 1–9 (2013)

    Google Scholar 

  12. Martinez, M., Stiefelhagen, R.: Automated multi-camera system for long term behavioral monitoring in intensive care units. In: Proceedings of the 13 IAPR International Conference on Machine Vision Applications, MVA, pp. 97–100 (2013)

    Google Scholar 

  13. Chase, J.G., Agogue, F., Starfinger, C., Lam, Z.H., Shaw, G.M., Rudge, A.D.: Quantifying agitation in sedated ICU patients using digital imaging. Comput. Method Prog. Biomed. 76, 131–141 (2004)

    Article  Google Scholar 

  14. Biswas, J., Jayachandran, M., Thang, P., Fook, V., Choo, T., Qiang, Q., Takahashi, S., Jianzhong, E., Feng, C., Yap, P.: Agitation monitoring of persons with dementia based on acoustic sensors, pressure sensors, ultrasound sensors,: a feasibility study. In: International Conference on Aging, Disability and Independence, pp. 3–15 (2006)

    Google Scholar 

  15. Wenhui, L., Weihong, Z., Zhiwei, Z., Qiang, J.: A real-time human stress monitoring system using dynamic bayesian network. In: CVPR 2005, pp. 70–77 (2005)

    Google Scholar 

  16. Sakr, G., Elhajj, I., Huijer, H.: Support vector machines to define and detect agitation transition. IEEE Trans. Affect. Comput. 1(2), 98–108 (2010)

    Article  Google Scholar 

  17. Pan, W., Yoshida, S., Liu, Q., Wu, C., Wang, J., Zhu, J., Cai, D.: Quantitative evaluation of severity of behavioral and psychological symptoms of dementia in patients with vascular dementia. Transl. Neurodegener. 2(9), 2–7 (2013)

    Google Scholar 

  18. Tractenberg, R., Singer, C., Cummings, J., Thal, L.: The sleep disorders inventory: an instrument for studies of sleep disturbance in persons with alzheimer’s disease. J. Sleep Res. 12(4), 331–337 (2003)

    Article  Google Scholar 

  19. Knuff, A.: Application of actigraphy to the measurement of neuropsychiatric symptoms of agitation in dementia. Master’s thesis, Queen’s University, Canada (2014)

    Google Scholar 

  20. Zhu, Y., Wenbin, C., Guodong, G.: Fusing spatiotemporal features and joints for 3d action recognition. In: CVPRW, pp. 486–491 (2013)

    Google Scholar 

  21. Bao, L., Intille, S.S.: Activity recognition from user-annotated acceleration data. In: Ferscha, A., Mattern, F. (eds.) PERVASIVE 2004. LNCS, vol. 3001, pp. 1–17. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  22. Stern, T.: Massachusetts General Hospital Handbook of General Hospital Psychiatry, 6th edn. Saunders, Elsevier, New York (2010)

    Google Scholar 

  23. Kwapisz, J., Weiss, G., Moore, S.: Activity recognition using cell phone accelerometers. SIGKDD Explor. Newsl. 12(2), 74–82 (2011)

    Article  Google Scholar 

  24. Opitz, D., Maclin, R.: Popular ensemble methods: an empirical study. J. Artif. Intell. Res. 11, 169–198 (1999)

    MATH  Google Scholar 

  25. Rodriguez, J., Kuncheva, L., Alonso, C.: Rotation forest: a new classifier ensemble method. IEEE Trans. Pattern Analy. Mach. Intell. 28(10), 1619–1630 (2006)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IATSL Laboratory, Toronto Rehab Institute, University of Toronto, Toronto, Canada

    Belkacem Chikhaoui, Bing Ye & Alex Mihailidis

Authors
  1. Belkacem Chikhaoui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bing Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alex Mihailidis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Belkacem Chikhaoui .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Carmelo R. García

  2. Departamento de Estadistica, Universidad La Laguna, La Laguna, Spain

    Pino Caballero-Gil

  3. Florida State University, Tallahassee, Florida, USA

    Mike Burmester

  4. University of Las Palmas de Gran Canaria, Las Palmas, Spain

    Alexis Quesada-Arencibia

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Chikhaoui, B., Ye, B., Mihailidis, A. (2016). Ensemble Learning-Based Algorithms for Aggressive and Agitated Behavior Recognition. In: García, C., Caballero-Gil, P., Burmester, M., Quesada-Arencibia, A. (eds) Ubiquitous Computing and Ambient Intelligence. IWAAL AmIHEALTH UCAmI 2016 2016 2016. Lecture Notes in Computer Science(), vol 10070. Springer, Cham. https://doi.org/10.1007/978-3-319-48799-1_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-48799-1_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48798-4

  • Online ISBN: 978-3-319-48799-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation