1. + Bayesian Networks-Based Interval Training Guidance System for Cancer Rehabilitation Myung-kyung Suh, Kyujoong Lee, Alfred Heu, Ani Nahapetian, Majid Sarrafzadeh University of California, Los Angeleås

2. + Intro Over 53.9% of cancer patients survive more than 5 years after surgeries. Many of these patients have a chronic illness. Cancer fatigue is seen most frequently. Results from muscle weakness, pain or sleep disruption. Causes disruptions in physical, emotional, and social functions. Many researchers and physicians recommend interval training Interval training helps improve aerobic capacity restore physical functions cardiovascular systems Interval training has been shown to decrease fatigue, and somatic complaints in recovering cancer patients [1]. Cancer Rehabilitation [1] Diemo FC. 1999. Effects of physical activity on the fatigue and psychological status of cancer patients during chemotherapy

3. + Intro Consists of interleaving high intensity exercises with rest periods Other Benefits weight loss general fitness the reduction of heart diseases Interval Training 3

4. + Intro Programmed treadmills and cycles Without them, there is almost no way to imitate a given exercise protocol. Without strong motivation, an individual can be discouraged from following an interval training protocol. Interval Training 4

5. + iPhone Interval Training Guidance System Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking 5 Customized Input

6. + iPhone Interval Training Guidance System Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking 6 Interval Training Game

7. + iPhone Interval Training Guidance System Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking 7 Music Recommendation

8. + iPhone Interval Training Guidance System Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking 8 Social Networking

9. + Interval Training Motivations Reduce space and cost restrictions compared with traditional fitness equipment iPhones easy interface 3.5 inch multi-touch display 480-by-320-pixel resolution 9 Light-Weight Wireless Smartphone Factors influencing mobile handheld device use and adoption

10. + Interval Training Motivations 10 Light-Weight Wireless Smartphone Factors influencing mobile handheld device use and adoption Network connection Modalities of mobility HSDPA (High-Speed Downlink Packet Access) to download data quickly over UMTS (Universal Mobile Telecommunications System) Using 3G network When not in a 3G network area, the iPhone uses a GSM network for calls and an EDGE network for data. According to the market research group NPD, Apple's iPhone 3G topped the sales charts

11. + Interval Training Motivations 11 Music Motivation Situational factors Personal factors Rhythm response Musicality Improved mood Arousal control Dissociation Reduced RPE Greater work output Improved skill acquisition Flow state Enhanced performance Terry, Peter C. and Karageorghis, Costas I., 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. 2006

12. + Interval Training Motivations SubscaleRanking Affiliation2 Appearance12 Challenge4 Competition1 Enjoyment3 Health pressures14 Ill-health avoidance13 Nimbleness8 Positive health7 Revitalization5 Social recognition9 Strength and endurance6 Stress management10 Weight management11 12 Competitive Group Exercise Exercising together Maintain affiliation with friends and promote more exercise Related to social network Ranking of exercise motivation 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

13. + Related Works Music Recommendation Systems Pandora MusicSurfer iPod Exercise Applications Nike + iPod Sport Kit Nike+ Shoes Social Network Systems FaceBook MySpace 13

14. + System Design Using the user input, the system comes up with a customized interval training protocol. By comparing the schedule with the exercise data collected from the 3-axis accelerometer, the accuracy or score of the exercise is calculated. 14 Game Scheduled interval training (a) and the accelerometer data for the exercise (b)

15. + System Design Content-based filtering Selects songs based on the correlation between the content of the items and the users preferences. 15 Music Recommendation

16. + Collaborative filtering Chooses songs based on the correlation among people with similar preferences. Uses Bayesian networks in our system. 16 System Design Music Recommendation

17. + In collaborative filtering The system classifies users based on age, gender, and residential location, etc. Songs are selected by using Bayesian networks. 17 System Design Music Recommendation Sources of variation in music preference LeBlanc, A., Tempo Preferences of Different Age Music Listeners. Journal of research in music education, 1988

18. + How Bayesian Networks Work? Based on the assumptions, a Bayesian network model is obtained and is used to calculate the probability that the given song is recommended by people sharing similarities with the user. When the value is above the threshold, the song is recommended to the user. 18 System Design Music Recommendation

19. + Context-aware filtering Provide a user with relevant information and services based on ones current context such as exercise intensity. 19 System Design Music Recommendation

20. + System Design E-mails containing the accuracy of the exercise sessions, exercise session time, and the amount of calories burned, etc. are sent to other members in the users social networking group 20 Social Network

21. + Experimental Results Individual 1Individual 2Individual 3Individual 4Individual 5Individual 6Individual 7Individual 8 GenderFemaleMale Female Male Female Age2524272825292725 Weight (kg) 5161.47349.550.5627051 Height (cm) 158170175163164172175158 Residential District Los Angeles, CA 21

22. + Experimental Results 22 Each song in the web database was annotated more than 8 times by 8 users. Compared with the method which recommends music preferred by people who share the same conditions, Bayesian networks- based recommendation method is better for selecting suitable exercise music. The number of refused songs among 10 recommendations for a 30 years old, 180cm, and 80kg individual living in Los Angeles, California.

23. + Conclusion 23

24. + Questions?? 24