Bayesian Networks-Based Interval Training Guidance System for Cancer Rehabilitation

1. Bayesian Networks-Based Interval Training Guidance Systemfor 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

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

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

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

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

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

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

10. Interval Training Motivations Light-Weight Wireless Smartphone 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 Factors influencing mobile handheld device use and adoption

11. Improved mood Arousal control Dissociation Reduced RPE Greater work output Improved skill acquisition Flow state Enhanced performance Situational factors Personal factors Rhythm response Musicality Interval Training Motivations Music Motivation 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 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

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. 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 user’s preferences. Music Recommendation

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

17. System Design In collaborative filtering The system classifies users based on age, gender, and residential location, etc. Songs are selected by using Bayesian networks. 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. System Design 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. Music Recommendation

19. System Design Context-aware filtering Provide a user with relevant information and services based on one’s current context such as exercise intensity. 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 user’s social networking group Social Network

21. Experimental Results

22. Experimental Results • 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

24. Questions??