+ 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
+ Bayesian Networks-Based Interval Training Guidance System for Cancer Rehabilitation Myung-kyung Suh, Kyujoong Lee, Alfred Heu, Ani Nahapetian, Majid.
Mar 29, 2015
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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
+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
+Intro
Consists of interleaving high intensity exercises with rest periods
Other Benefits weight loss general fitness the reduction of heart diseases
Interval Training
3
+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
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+iPhone Interval Training Guidance System
Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking
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Customized Input
+iPhone Interval Training Guidance System
Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking
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Interval Training Game
+iPhone Interval Training Guidance System
Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking
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Music Recommendatio
n
+iPhone Interval Training Guidance System
Our behavioral cueing system developed for the iPhone uses music, sensor readings, and social networking
8
Social Networking
+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
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Light-Weight Wireless SmartphoneFactors influencing mobile handheld device use and adoption
+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
+Interval Training Motivations
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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
+Interval Training Motivations
Subscale RankingAffiliation 2Appearance 12Challenge 4
Competition 1Enjoyment 3
Health pressures 14Ill-health avoidance 13
Nimbleness 8Positive health 7Revitalization 5
Social recognition 9Strength and endurance 6
Stress management 10Weight management 11
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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
+Related Works
Music Recommendation Systems Pandora MusicSurfer
iPod Exercise Applications Nike + iPod Sport Kit Nike+ Shoes
Social Network Systems FaceBook MySpace
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+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.
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Game
Scheduled interval training (a) and the accelerometer data for the exercise (b)
+System Design
Content-based filtering Selects songs based on the
correlation between the content of the items and the user’s preferences.
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Music Recommendation
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Collaborative filtering Chooses songs based on the
correlation among people with similar preferences.
Uses Bayesian networks in our system.
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System DesignMusic Recommendation
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In collaborative filtering The system classifies users based on age, gender, and residential location,
etc. Songs are selected by using Bayesian networks.
17System DesignMusic Recommendation
Sources of variation in music preferenceLeBlanc, A., Tempo Preferences of Different Age Music Listeners. Journal of research in music education, 1988
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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.
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System DesignMusic Recommendation
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Context-aware filtering Provide a user with relevant information and services based
on one’s current context such as exercise intensity.
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System DesignMusic Recommendation
+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
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Social Network
Contact List
System User
Group
Friends who compete with the user
User Databse
Contact List
System User
Group
Friends who compete with the user
User Databse
+Experimental Results
Individual 1 Individual 2 Individual 3 Individual 4 Individual 5 Individual 6 Individual 7 Individual 8
Gender Female Male Male Female Female Male Male Female
Age 25 24 27 28 25 29 27 25
Weight(kg)
51 61.4 73 49.5 50.5 62 70 51
Height(cm)
158 170 175 163 164 172 175 158
Residential District
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
Los Angeles, CA
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+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.
+Conclusion
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+Questions??
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