Cited By
View all- Şahin FYan Z(2018)Mobile Phones in Data CollectionInternational Journal of Cyber Behavior, Psychology and Learning10.4018/ijcbpl.20130701063:3(67-87)Online publication date: 13-Dec-2018
Simple statistical inference algorithms for task-dependent wellness assessment
Pages 725 - 734
Abstract
Stress is a key indicator of wellness in human beings and a prime contributor to performance degradation and errors during various human tasks. The overriding purpose of this paper is to propose two algorithms (probabilistic and non-probabilistic) that iteratively track stress states to compute a wellness index in terms of the stress levels. This paper adopts the physiological view-point that high stress is accompanied with large deviations in biometrics such as body temperature, heart rate, etc., and the proposed algorithms iteratively track these fluctuations to compute a personalized wellness index that is correlated to the engagement levels of the tasks performed by the user. In essence, this paper presents a quantitative relationship between temperature, occupational stress, and wellness during different tasks. The simplicity of the statistical inference algorithms make them favorable candidates for implementation on mobile platforms such as smart phones in the future, thereby providing users an inexpensive application for self-wellness monitoring for a healthier lifestyle.
References
[1]
Takei, H., Horguchi, S., Shimizu, T., Hayashi, Y., Takahashi, A. and Tomizawa, Y., Wellness support platform using mobile terminals. NTT DOCOMO Tech. J. v11 i2. 9-15.
[2]
Kailas, A., Chong, C.-C. and Watanabe, F., From mobile phones to personal wellness dashboards. IEEE Eng. Med. Biol. Soc. Mag. (IEEE Pulse). v1 iJuly-August (1). 57-63.
[3]
Wellness mobile phone measures body fat, pulse. Source: {http://techon.nikkeibp.co.jp/english/NEWS_EN/20071004/140249/}.
[4]
P. Panzarino, Stress symptoms, causes, signs, types, management, and treatment, 2010. Source: {http://www.medicinenet.com}.
[5]
Eisdorfer, C. and Elliott, G.R., Stress and Human Health: Analysis and Implications of Research: A Study. 1982. Springer.
[6]
Frankenhaeuser, M., A psychobiological framework for research on human stress and coping. In: Mortimer, H., Trumbull, R. (Eds.), Dynamics of Stress: Physiological, Psychological, and Social Perspectives. The Plenum Series on Stress and Coping, vol. xvii. Plenum Press Appley, New York, NY, US. pp. 101-116.
[7]
J. Healy, R. Picard, Smartcar: detecting driver stress, in: Proceedings of the 15th International Conference on Pattern Recognition, 2000, pp. 218-221.
[8]
Li, X. and Ji, Q., Active affective state detection and user assistance with dynamic Bayesian networks. IEEE Trans. Syst. Man Cybern. A Syst. Hum. v35 iJanuary (1). 93-105.
[9]
Liao, W., Zhong, W., Zhu, Z., Ji, Q. and Gray, W., Toward a decision-theoretic framework for affect recognition and user assistance. Int. J. Hum. Comput. Stud. v64. 847-873.
[10]
M. Rimini-Doering, D. Manstetten, T. Altmueller, U. Ladstaetter, M. Mahler, Monitoring driver drowsiness and stress in a driving simulator, in: Proceedings of the 1st International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, 2001, pp. 58-63.
[11]
Rani, P., Sims, J., Brackin, R. and Sarkar, N., Online stress detection using psychophysiological signals for implicit human robot cooperation. Robotica. v20. 673-685.
[12]
G. Rigas, C.D. Katsis, P. Bougia, D.I. Fotiadis, A reasoning-based framework for car drivers stress prediction, in: Proceedings of the 16th Mediterranean Conference on Control and Automation, 2008, pp. 627-632.
[13]
H. Kataoka, H. Yoshida, A. Saijo, M. Yasuda, M. Osumi, Development of a skin temperature measuring system for non-contact stress evaluation, in: Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 2, 1998, pp. 940-943.
[14]
Bligh, J., The thermosensitivity of the hypothalamus and thermoregulation in mammals. Biol. Rev. v41 iJanuary (3). 317-365.
[15]
J. James, C. Baker, H. Swain, Principles of science for nurses. Source: {http://www3.interscience.wiley.com/cgibin/summary/117908703/SUMMARY?CRETRY=1&SRETRY=0}.
[16]
Craven, R.F. and Hirnle, C.J., Fundamentals of Nursing: Human Health and Function. 2008. Lippincott Williams & Wilkins, USA.
[17]
J.F. O'Brien, T.E. Bernard, W.L. Kenney, Personal monitor to protect workers from heat stress, in: Proceedings of the Conference on Human Factors and Power Plants, 1988.
[18]
M.H. Lee, G. Yang, H.K. Lee, S. Bang, Development stress monitoring system based on personal digital assistant (PDA), in: Proceedings of the Engineering in Medicine and Biology Society, September 2004, pp. 2364-2367.
[19]
D.G. Park, S.C. Shin, S.W. Kang, Y.T. Kim, Development of flexible self adhesive patch for professional heat stress monitoring service, in: Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, September 2005, pp. 3789-3792.
[20]
I.G. Finvers, J.W. Haslett, G. Jullien, Wireless temporal artery bandage thermometer, in: Proceedings of the Biomedical Circuits and Systems Conference 2006, pp. 166-169.
[21]
Probabilistic assessment of user's emotions in educational games. J. Appl. Artif. Intell. v16. 555-575.
[22]
J. Fang, S. Tan, B. Ahmed, D. Berlian, I. Cosic, A PDA based ambulatory human skin resistance measuring system, in: Proceeding of IASTED International Conference on Telehealth, 2005.
[23]
Jones, L.A. and Ho, H.-N., Warm or cool, large or small? The challenge of thermal displays. IEEE Trans. Haptics. v1 iJanuary-June (1). 53-70.
[24]
Zhang, Y. and Ji, Q., Active and dynamic information fusion for facial expression understanding from image sequence. IEEE Trans. Pattern Anal. Mach. Intell. v27 i5. 699-714.
[25]
Searle, B.J., Bright, J.E. and Bochner, S., Testing the three-factor model of occupational stress: the impacts of demands, control and social support on a mail sorting activity. Work Stress. v20. 268-279.
[26]
Source: {http://www.kidzmed.com/ccp0-prodshow/01500.html}.
[27]
Salvador, A., Coping with competitive situations in humans. Neurosci. Biobehav. Rev. v29 iFebruary (1). 195-205.
Recommendations
A Smart Approach to Medication Management
Smart medication management systems can remotely monitor and regulate patients' prescription drug use, support communication with caregivers to improve scheduling and increase motivation, and generate context-aware reminders.
A Novel CNN-TLSTM Approach for Dengue Disease Identification and Prevention using IoT-Fog Cloud Architecture
AbstractOne of the mosquito-borne pandemic viral infections is Dengue which is mostly transmitted to humans by the Aedes agypti or female Aedes albopictis mosquitoes. The dengue disease expansion is mainly due to the different factors such as climate ...
Designing wellness interventions and applications
CHI EA '12: CHI '12 Extended Abstracts on Human Factors in Computing SystemsIn recent years, we have seen a large explosion of wellness interventions and technology applications focused on human's well-being with the intention of helping people avoid needing medical care. Given the increasing emergence of wellness applications, ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © Elsevier Ltd © 2012.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 01 July 2012
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 03 Oct 2024
Other Metrics
Citations
Cited By
View all- Şahin FYan Z(2018)Mobile Phones in Data CollectionInternational Journal of Cyber Behavior, Psychology and Learning10.4018/ijcbpl.20130701063:3(67-87)Online publication date: 13-Dec-2018
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in