Abstract
Health care costs and ageing population are two factors which are of major concern to western governments in the 21st century. Existing work in affective health care is primary focused on developing avatars in the tele-health space. This paper reports on the modeling of emotions (anxiety level) of patients in pre-operative stage using communication robots to assist nurses in providing personalized care and determining the sedation level of patients. The research is based on the need for better management of IT resources for social innovation as well as need to improve operational efficiency and quality of care.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Harkness, J.: Patient involvement: a vital principle for patient-centered health care. World Hosp. Health Serv. 41(2), 12-6, 40-3 (2005)
Khosla, R., Sethi, I., Damiani, E.: Intelligent Multimedia Multi-Agent Systems- A Human-Centered Approach, 333 pages. Kluwer Academic Publishers, Massachusetts (2000)
Lisetti, C., Nasoza, F., LeRougeb, C., Ozyera, O., Alvarezc, K.: Developing multimodal intelligent affective interfaces for tele-home health care. International Journal of Human-Computer Studies 59(1-2), 245–255 (2003)
Picard, R.: Affective Computing. MIT Press, Cambridge (1997)
Shapiro, S.L., Astin, J.A., Bishop, S.R., Cordova, M.: Mindfulness-Based Stress Reduction for Health Care Professionals: Results from a Randomized Trial. International Journal of Stress Management 12(2), 164–176 (2005)
Klein, G., Moon, B., Hoffman, R.R.: Making Sense of Sensemaking 1: Alternative Perspectives. In: IEEE Intelligent Systems, pp. 70–73 (2006), doi:10.1109/MIS.2006.75
Helfenstein, S.: Product Meaning, Affective Use Evaluation, and Transfer: A Preliminary Study. Human Technology 1(1), 76–100 (2005)
Rasmussen, J.: Information Processing and Human-Machine Interaction: An Approach to Cognitive Engineering. North-Holland, Amsterdam (1986)
Irfan, A., et al.: Coding, Analysis, Interpretation, and Recognition of Facial Expressions. IEEE Trans. on Pattern analysis and Machine Intelligence 19(7) (July 1997)
Lang, P.J.: The emotion probe: Studies of motivation and attention. American Psychologist 50(5), 372–385 (1995)
Facial Action Coding System, http://www.cs.cmu.edu/afs/cs/project/face/www/facs.htm
Swindale, J.E.: The nurse’s role in giving pre-operative information to reduce anxiety in patients admitted to hospital for elective minor surgery. Journal of Advanced Nursing 14, 899–905 (1989)
Khosla, R., Chowdhury, B.: Real-Time RFID-Based Intelligent Healthcare Diagnosis System. In: Zhang, D. (ed.) ICMB 2008. LNCS, vol. 4901, pp. 184–191. Springer, Heidelberg (2007)
Lisetti, C., LeRouge, C.: Affective Computing in Tele-home Health. In: 37th Hawaii International Conference on System Sciences – 2004, pp. 1–8 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Khosla, R., Chu, M.T., Denecke, K., Yamada, K., Yamaguchi, T. (2010). Assistive Communication Robot for Pre-operative Health Care. In: Zhang, D., Sonka, M. (eds) Medical Biometrics. ICMB 2010. Lecture Notes in Computer Science, vol 6165. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13923-9_24
Download citation
DOI: https://doi.org/10.1007/978-3-642-13923-9_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13922-2
Online ISBN: 978-3-642-13923-9
eBook Packages: Computer ScienceComputer Science (R0)