Abstract
The number of cancer patients who live more than 5 years after surgery exceeds 53.9% over the period of 1974 and 1990; Treatments for cancer patients are important during the recovery period, as physical pain and cancer fatigue affect cancer patients’ psychological and social functions. Researchers have shown that interval training improves the physical performance in terms of fatigue level, cardiovascular build-up, and hemoglobin concentration, the feelings of control, independence, self-esteem, and social relationship during cancer rehabilitation and chemotherapy periods. The lack of proper individual motivation levels and the difficulty in following given interval training protocols results in patients stopping interval training sessions before reaching proper exhaustion levels.
In this work, we use behavioral cueing using music and performance feedback, combined with a social network interface, to provide motivation during interval training exercise sessions. We have developed an application program on the popular lightweight iPhone platform, embedded with several leveraged sensors. By measuring the exercise accuracy of the user through sensor readings, specifically accelerometers embedded in the iPhone, we are able to play suitable songs to match the user’s workout plan. A hybrid of a content-based, context-aware, and collaborative filtering methods using Bayesian networks incorporates the user’s music preferences and the exercise speed that will enhance performance. Additionally, exercise information such as the amount of calorie burned, exercise time, and the exercise accuracy, etc. are sent to the user’s social network group by analyzing contents of the web database and contact lists in the user’s iPhone.
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References
Lehmann, J.F., DeLisa, J.A., Warren, C.G., et al.: Cancer rehabilitation: assessment of need, development, and evaluation of a model of care. In: Archives of physical medicine and rehabilitation (1978)
Gerber, L.H.: Cancer rehabilitation into the future. Cancer (2001)
Dimeo, F.C.: Aerobic exercise in the rehabilitation of cancer patients after high dose chemotherapy and autologous peripheral stem cell transplantation. Cancer (1997)
Dimeo, F.C.: Effects of physical activity on the fatigue and psychologic status of cancer patients during chemotherapy. Cancer (1999)
Gorostiaga, E.M., Walter, C.B., Foster, C., et al.: Uniqueness of interval and continuous training at the same maintained exercise intensity. Eur. J. Appl. Physiol. (1991)
Astrand, I., Astrand, P.O., Christensen, E.H., et al.: Intermittent muscular work. Acta Physiol. Scand (1960)
Billat, V., Slawinski, J., Bocquet, V., et al.: Intermittent runs at vV. O2max enables subjects to remain at VO2max for a longer time than submaximal runs. Eur. J. Appl. Physiol. (2000)
Essen, B., Hagenfeldt, L., Kaijser, L.: Utilization of blood-born and intra-muscular substrates during continuous and intermittent exercise in man. J. Physiol, Lond (1977)
Fox, E.L., et al.: Frequency and duration of interval training programs and changes in aerobic power. Journal of Applied Physiology 38(3)
Reindell, H., Roskamm, H., Gerschler, W.: Das Interval training. John Ambrosius Barth Publishing, Munchen (1962)
Billat, L.V.: Interval Training for Performance: A Scientific and Empirical PracticeSpecial Recommendations for Middle- and Long-Distance Running. Part I: Aerobic Interval Training. Sports Med. (2001)
Terry, P.C., Karageorghis, C.I.: Psychophysical effects of music in sport and exercise: an update on theory, research and application. In: Joint Conference of the Australian Psychological Society and the New Zealand Psychological Society (2006)
Karageorghis, C.I., Terry, P.C.: The magic of music in movement. Sport and Medicine Today (2001)
Mohammadzadeh, H., Tartibiyan, B., Ahmadi, A.: The effects of music on the perceived exertion rate and performance of trained and untrained individuals during progressive exercise. Physical Education and Sport (2008)
Kilpatrick, M.: College Students’ Motivation for Physical Activity: Differentiating Men’s and Women’s Motives for Sport Participation and Exercise. Journal of American college health (2005)
Williams, P.: Effects of group exercise on cognitive functioning and mood in older women. Australian and New Zealand Journal of Public Health (1997)
Sarker, S., Wells, J.D.: Understanding mobile handheld device use and adoption. Communications of the ACM (2003)
Kleijnen, M., de Ruyter, K., Wetzels, M.G.M.: Factors influencing the adoption of mobile gaming services. In: Mennecke, B.E., Strader, T.J. (eds.) Mobile Commerce, Idea (2003)
Knopf, R.C.: Recreational needs and behavior in natural settings. In: Altman, I., Wohlwill, J.F. (eds.) Human behavior and environment: behavior and the natural environment, vol. 6. Plenum Press, New York (1983)
Knopf, R.C.: Human behavior, cognition, and affect in the natural environment. In: Stokols, D., Altman, I. (eds.) Handbook of environmental psychology, vol. 1. Wiley, Chichester (1987)
Kaplan, R., Kaplan, S.: The experience of nature: a psychological perspective. Cambridge University Press, Cambridge (1989)
Kristoffersen, S., Ljungberg, F.: Mobility: From stationary to mobile work. In: Braa, K., Sorensen, C., Dahlbom, B. (eds.) Planet Internet (2000)
Apple iPhone: http://www.apple.com/iphone/
Park, H.: A context-aware music recommendation system using fuzzy bayesian networks with utility theory. LNCS. Springer, Heidelberg (2006)
Cano, P., et al.: Content-based music audio recommendation. In: Proc. ACM Multimedia (2005)
Van Setten, M.: Context-aware recommendations in the mobile tourist application COMPASS. LNCS. Springer, Heidelberg (2004)
Brunato, M.: PILGRIM: A Location Broker and Mobility-Aware Recommendation System. In: Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (2003)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE T Knowl. Data En. (2005)
Balabanović, M.: Fab: content-based, collaborative recommendation. Communications of the ACM (1997)
Mooney, R.J.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries (2000)
LeBlanc, A.: Tempo Preferences of Different Age Music Listeners. Journal of research in music education (1988)
LeBlanc, A., Sims, W.L., Siivola, C., Obert, M.: Music style preferences of different age listeners. Journal of Research in Music Education (1996)
Christenson, P.G., Peterson, J.B.: Genre and gender in the structure of music preferences. Communication Research (1988)
Johnstone, J., Katz, E.: Youth and popular music: A study in the sociology of taste. American Journal of Sociology (1957)
Furman, W.: Age and sex differences in perceptions of networks of personal relationships. Child development (1992)
Hale, S.: Global processing-time coefficients characterize individual and group differences in cognitive speed. Psychological science (1994)
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Suh, Mk., Lee, K., Heu, A., Nahapetian, A., Sarrafzadeh, M. (2010). Bayesian Networks-Based Interval Training Guidance System for Cancer Rehabilitation. In: Phan, T., Montanari, R., Zerfos, P. (eds) Mobile Computing, Applications, and Services. MobiCASE 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12607-9_16
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DOI: https://doi.org/10.1007/978-3-642-12607-9_16
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