Skip to main content

Automated Behavioral Mapping for Monitoring Social Interactions among Older Adults

  • Conference paper
Book cover Social Robotics (ICSR 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7621))

Included in the following conference series:

Abstract

Social interactions in retirement communities’ shared spaces is a key component to preventing social isolation and loneliness among older people. Given the underutilization of these spaces, placing technologies to promote socialization in shared spaces might improve independence and quality of life among older adults. In order to understand socializations in these shared spaces, surveillance systems must be developed to quantify the number and type of interactions in an environment. We hypothesize that social interactions amongst older adults can be detected using multiple cameras and microphones strategically placed in the environment. The purpose of this paper is to describe the development of an automatic behavioral mapping surveillance system designed for monitoring interactions among older adults and technology interventions in retirement communities’ shared common areas. Specific emphasis is given to the system designed to monitor the number, length and type of interactions of older adults in the community.

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

Access this chapter

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 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Arif, O., Vela, P.: Robust density comparison for visual tracking. In: British Machine Vision Conference (2009)

    Google Scholar 

  2. Chen, D., Yang, J., Malkin, R., Wactlar, H.D.: Detecting social interactions of the elderly in a nursing home environment. ACM Trans. Multimedia Comput. Commun. Appl., 3 (2007)

    Google Scholar 

  3. Ephraim, Y., Malah, D.: Speech enhancement using a minimum-mean square error short-time spectral amplitude estimator. IEEE Trans. on Acoustics, Speech and Signal Processing 32(6), 1109–1121 (1984)

    Article  Google Scholar 

  4. Golden, J., Conroy, R.M., Bruce, I., Denihan, A., Greene, E., Kirby, M., Lawlor, B.A.: Loneliness, social support networks, mood and wellbeing in community-dwelling elderly. Int. J. of Geriatric Psychiatry 24(7), 694–700 (2009)

    Article  Google Scholar 

  5. Hauptmann, A.G., Gao, J., Yan, R., Qi, Y., Yang, J., Wactlar, H.D.: Automated analysis ofnursing home observations. IEEE Pervasive Computing 3(2), 15–21 (2004)

    Article  Google Scholar 

  6. Lymberopoulos, D., Bamis, A., Savvides, A.: Extracting spatiotemporal human activity patterns in assisted living using a home sensor network. In: Int. Conf. on Pervasive Technologies Related to Assistive Environments, pp. 29:1–29:8. ACM, New York (2008)

    Google Scholar 

  7. Martin, R.: Noise power spectral density estimation based on optimal smoothing and minimum statistics. IEEE Trans. on Speech and Audio Processing 9(5), 504–512 (2001)

    Article  Google Scholar 

  8. Nehmer, J., Lindenberger, U., Steinhagen-Thiessen, E.: Aging and Technology - Friends, Not Foes. Journal of Gerontopsychology and Geriatric Psychiatry 23(2), 55–57 (2010)

    Article  Google Scholar 

  9. Observer XT, http://www.noldus.com/human-behavior-research/products/the-observer-xt

  10. Park, S., Kautz, H.: Privacy-preserving Recognition of Activities in Daily Livingfrom Multi-view Silhouettesand RFID-based Training. In: AAAISymposium on AI in Eldercare: New Solutions to Old Problems (2008)

    Google Scholar 

  11. Sohn, J., Kim, N.S., Sung, W.: A Statistical Model-based Voice Activity Detection. IEEE Signal Processing Letters 6(1), 1–3 (1999)

    Article  Google Scholar 

  12. Stauffer, C., Grimson, W.E.L.: Adaptive Background Mixture Models for Real-Time Tracking. In: Conf. on Computer Vision and Pattern Recognition, pp. 252–268 (1999)

    Google Scholar 

  13. Wu, C., Khalili, A.H., Aghajan, H.: Multiview Activity Recognition in Smart Homes with Spatio-temporal Features. In: ACM/IEEE Int. Conf. on Distributed Smart Cameras, pp. 142–149. ACM, New York (2010)

    Chapter  Google Scholar 

  14. Wu, J., Osuntogun, A., Choudhury, T., Philipose, M., Rehg, J.M.: A Scalable Approach to Activity Recognition Based onObject Use. In: Int. Conf. on Computer Vision, Rio de Janeiro (2007)

    Google Scholar 

  15. Yang, J., Shi, Z., Vela, P.A.: Person Reidentification by Kernel PCA Based Appearance Learning. In: Canadian Conf.on Computer and Robot Vision, pp. 227–233 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Rebola, C.B., Ogunmakin, G., Vela, P.A. (2012). Automated Behavioral Mapping for Monitoring Social Interactions among Older Adults. In: Ge, S.S., Khatib, O., Cabibihan, JJ., Simmons, R., Williams, MA. (eds) Social Robotics. ICSR 2012. Lecture Notes in Computer Science(), vol 7621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34103-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-34103-8_19

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34102-1

  • Online ISBN: 978-3-642-34103-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics