Mobile Networks and Applications

, Volume 17, Issue 2, pp 163–177 | Cite as

Machine Learning-Based Adaptive Wireless Interval Training Guidance System

  • Myung-kyung SuhEmail author
  • Ani Nahapetian
  • Jonathan Woodbridge
  • Mahsan Rofouei
  • Majid Sarrafzadeh


Interval training has been shown to improve the physical and psychological performance of users, in terms of fatigue level, cardiovascular build-up, hemoglobin concentration, and self-esteem. Despite the benefits, there is no known automated method for formulating and tailoring an optimized interval training protocol for a specific individual that maximizes the amount of calories burned while limiting fatigue. Additionally, an application that provides the aforementioned optimal training protocol must also provide motivation for repetitious and tedious exercises necessary to improve a patient’s adherence. This paper presents a system that efficiently formulates an optimized interval training method for each individual by using data mining schemes on attributes, conditions, and data gathered from individuals exercise sessions. This system uses accelerometers embedded within iPhones, a Bluetooth pulse oximeter, and the Weka data mining tool to formulate optimized interval training protocols and has been shown to increase the amount of calories burned by 29.54% as compared to the modified Tabata interval training protocol. We also developed a behavioral cueing system that uses music and performance feedback to provide motivation during interval training exercise sessions. By measuring a user’s performance through sensor readings, we are able to play songs that match the user’s workout plan. A hybrid collaborative, content, and context-aware filtering algorithm incorporates the user’s music preferences and the exercise speed to enhance performance.


wireless health embedded system exercise guidance system interval training wearable wireless sensors music recommendation social networks rehabilitation data mining Bayesian-network J48 tree 



This research was supported by NLM (National Library of Medicine).

I would like to thank Alfred Heu and Kyujoong Lee for their help in performing the provided experiments, and to Professor William Kaiser for his helpful advice.


  1. 1.
    Billat LV (2001) Interval training for performance: a scientific and empirical practice: special recommendations for middle-and long-distance running. Part I: aerobic interval training. Sports Med 31(1):13–31CrossRefGoogle Scholar
  2. 2.
    Gorostiaga EM (1991) Uniqueness of interval and continuous training at the same maintained exercise intensity. Eur J Appl Physiol 63(2):101–107CrossRefGoogle Scholar
  3. 3.
    Essen B (1977) Utilization of blood-borne and intramuscular substrates during continuous and intermittent exercise in man. J Physiol 265(2):489–506Google Scholar
  4. 4.
    Fox EL (1975) Frequency and duration of interval training programs and changes in aerobic power. J Appl Physiol Respir Environ Exerc Physiol 38(3):481–484Google Scholar
  5. 5.
    Dimeo FC (1999) Effects of physical activity on the fatigue and psychologic status of cancer patients during chemotherapy. Cancer 85(10):2273–2277CrossRefGoogle Scholar
  6. 6.
    Tabata I (1996) Effects of moderate-intensity endurance and high-intensity intermittent training on anaerobic capacity and [spacing dot above] VO2max. Med Sci Sports Exerc 28(10):1327–1330CrossRefGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    Tapia, E. M. (2007). Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. The 11th International Conference on Wearable Computers.Google Scholar
  10. 10.
    Terry, P. C. (2006). Psychophysical effects of music in sport and exercise: an update on theory, research and application, Joint Conference of the Australian Psychological Society and the New Zealand Psychological Society.Google Scholar
  11. 11.
    Karageorghis, C. I. (2001). The magic of music in movement. Sport and Medicine Today.Google Scholar
  12. 12.
    Kleijnen, M. (2003). Factors influencing the adoption of mobile gaming services. In Mobile Commerce: technology, theory, and applications. 202–217.Google Scholar
  13. 13.
    Cano, P. (2005). Content-based music audio recommendation. Proc. ACM Multimedia, pp. 212–212.Google Scholar
  14. 14.
  15. 15.
    Raymond, J. M. (2000) Content-based book recommending using learning for text categorization. In Proceedings of the Fifth ACM Conference on Digital Libraries.Google Scholar
  16. 16.
    Kwon OB (2003) “I know what you need to buy”: context-aware multimedia-based recommendation system. Expert Syst Appl 25(3):387–400CrossRefGoogle Scholar
  17. 17.
    Van Setten, M. (2004). Context-aware recommendations in the mobile tourist application COMPASS. Lecture notes in computer science, 235.Google Scholar
  18. 18.
    Park H (2006) A context-aware music recommendation system using fuzzy bayesian networks with utility theory. Lect Notes Comput Sci 4223:970–979CrossRefGoogle Scholar
  19. 19.
    Yang WS, Cheng HC, Dia JB (2008) A location-aware recommender system for mobile shopping environments. Expert Syst Appl 34(1):437–445CrossRefGoogle Scholar
  20. 20.
    Kilpatrick M (2005) College students’ motivation for physical activity: Differentiating men’s and women’s motives for sport participation and exercise. J Am Coll Health 54(2):87–94CrossRefGoogle Scholar
  21. 21.
    Williams P (1997) Effects of group exercise on cognitive functioning and mood in older women. Aust N Z J Public Health 21:45–52CrossRefGoogle Scholar
  22. 22.
    Quinlan JR (1993) C4.5: Programs for machine learning. Morgan Kaufmann, San MateoGoogle Scholar
  23. 23.
    Sarker S (2003) Understanding mobile handheld device use and adoption. Comm ACM 46(12):35–40CrossRefGoogle Scholar
  24. 24.
    Knopf, R. C. (1983). Recreational needs and behavior in natural settings. Human behavior and environment: behavior and the natural environment. Vol. 6.Google Scholar
  25. 25.
    Knopf, R. C. (1987). Human behavior, cognition, and affect in the natural environment. Handbook of environmental psychology Vol. 1.Google Scholar
  26. 26.
    Kaplan, S. (1989). The experience of nature: A psychological perspective.Google Scholar
  27. 27.
    Hasan, M. (2008). The effects of music on the perceived exertion rate and performance of trained and untrained individuals during progressive exercise. Physical Education and Sport.Google Scholar
  28. 28.
    Szabo A (1999) The effects of slow- and fast-rhythm classical music on progressive cycling to voluntary physical exhaustion. J Sports Med 39(3):220–225Google Scholar
  29. 29.
    Wijnalda, G. (2005). A personalized music system for motivation in sport performance. IEEE pervasive computing.Google Scholar
  30. 30.
  31. 31.
  32. 32.
    Cole CR (1999) Heart-rate recovery immediately after exercise as a predictor of mortality. N Engl J Med 341(18):1351–1357CrossRefGoogle Scholar
  33. 33.
    Mizuo J (2000) Exponential Hyperbolic Sine Function Fitting of Heart Rate Response to Constant Load Exercise. Jpn J Physiol 50(4):405–412CrossRefGoogle Scholar
  34. 34.
    Borg G (1987) Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol 56(6):679–685CrossRefGoogle Scholar
  35. 35.
    Janssen, P. G. J. M. (2001). Lactate threshold training. Human Kinetics.Google Scholar
  36. 36.
    van Ravenswaaij-Arts C (1993) Heart rate variability. Ann Intern Med 118(6):436–447Google Scholar
  37. 37.
    Lund-Johansen P (1999) Blood pressure and heart rate responses during physical stress in hypertension: modifications by drug treatment. Eur Heart J 1(B):10–17Google Scholar
  38. 38.
    Tuininga YS (1994) Heart rate variability in left ventricular dysfunction and heart failure: effects and implications of drug treatment. British heart journal 72(6):509–513CrossRefGoogle Scholar
  39. 39.
    Poole, D. C. (1985). Response of ventilatory and lactate thresholds to continuous and interval training. J Appl Physiol 58(4):1115–1121Google Scholar
  40. 40.
    Borg, G. (1987). Perceived exertion related to heart rate and blood lactate during arm and leg exercise. Eur J Appl Physiol 56(6):679–685Google Scholar
  41. 41.
    Prather, J. C. (1997). Medical data mining: knowledge discovery in a clinical data warehouse. AMIA Annual Fall Symposium 101–105.Google Scholar
  42. 42.
    LeBlanc A (1988) Tempo preferences of different age music listeners. J Res Music Educ 36(3):156–168CrossRefGoogle Scholar
  43. 43.
    LeBlanc A (1996) Music style preferences of different age listeners. J Res Music Educ 44(1):49–59CrossRefGoogle Scholar
  44. 44.
    Christenson, P. G. (1988) Genre and gender in the structure of music preferences. Comm Res 15(3):282–301CrossRefGoogle Scholar
  45. 45.
    Furman W (1992) Age and sex differences in perceptions of networks of personal relationships. Child Dev 63(1):103–115CrossRefGoogle Scholar
  46. 46.
    Johnstone J (1957) Youth and popular music: a study in the sociology of taste. Am J Sociol 62(6):563–568CrossRefGoogle Scholar
  47. 47.
    Hale S (1994) Global processing-time coefficients characterize individual and group differences in cognitive speed. Psychol Sci 5(6):384–389CrossRefGoogle Scholar
  48. 48.
    Wisloff U (2007) Superior cardiovascular effect of aerobic interval training versus moderate continuous training in heart failure patients: a randomized study. Circulation 115(24):3086–3094CrossRefGoogle Scholar
  49. 49.
    MacVicar MG (1989) Effects of aerobic interval training on cancer patients’ functional capacity. Nurs Res 38(6):348–51CrossRefGoogle Scholar
  50. 50.
    Witham MD (2003) Exercise training as a therapy for chronic heart failure: can older people benefit? J Am Geriatr Soc 51(5):6CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Myung-kyung Suh
    • 1
    Email author
  • Ani Nahapetian
    • 2
    • 3
  • Jonathan Woodbridge
    • 1
  • Mahsan Rofouei
    • 1
  • Majid Sarrafzadeh
    • 1
    • 4
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA
  2. 2.Computer Science DepartmentCalifornia State University, Northridge (CSUN)Los AngelesUSA
  3. 3.Computer Science Department, UCLALos AngelesUSA
  4. 4.Wireless Health InstituteUniversity of CaliforniaLos AngelesUSA

Personalised recommendations