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Department of Computer and Electrical Engineering. A Study of Time-based Features and Regularity of Manipulation to Improve the Detection of Eating Activity Periods During Free-Living. MS Defense Exam Jose Luis Reyes. Dr. Adam Hoover (chair) Dr. Eric Muth Dr. Richard Groff. April 24, 2014. - PowerPoint PPT Presentation
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Department of Computer and Electrical Engineering
A Study of Time-based Features and Regularity of Manipulation to Improve the Detection of Eating Activity
Periods During Free-Living
MS Defense ExamJose Luis Reyes
Dr. Adam Hoover (chair)Dr. Eric Muth
Dr. Richard Groff
April 24, 2014
OutlineMotivation and BackgroundDesign and MethodsResultsConclusion
Obesity• Common
– 34% of U.S. population are obese [Centers for Disease Control and Prevention]
• Serious– 5th leading risk for global deaths [WHO, 2014]– Heart disease, stroke, type 2 diabetes, and certain types
of cancer [Centers for Disease Control and Prevention]• Costly
– In 2008, annual medical cost was $147 billion in the U.S. [Centers for Disease Control and Prevention]
– In 2008, medical cost was $1,429 higher than of those of normal weight. [Centers for Disease Control and Prevention]
Obesity treatmentsDietary changesExercise and activityBehavior changesWeight-loss medicationWeight-loss surgeryLimit energy intake (EI)*
Balancing EI and EE (energy expenditure)
Monitoring EIMost widely used tools
Food diary24-hour recallFood frequency questionnaire
Technology-based toolsCamera [Martin et al., 2009]Wearable sensors [Amft et al., 2008]
Bite Counter
Watch-like deviceWrist motion trackingAccelerometer and gyroscope
Previous work
Goal: Detection of eating activity periodsBased on accelerometer (AccX, AccY, AccZ)
and gyroscope (Yaw, Pitch, Roll) readingsData segmentationClassification of eating activity (EA) and non-
eating activity (non-EA) periods based on features
Overall accuracy obtained was 81%
NoveltyPrevious work considered only sensor-based
featuresWe consider the time component
Time since last eating activityCumulative eating time
Periodicity of manipulation over timeRegularity of manipulation
Design and methodsOverview of algorithmData collectionNew features
Regularity of manipulationTime since last EACumulative eating time
Evaluation metrics
Overview of algorithm (Dong et al., 2013) •Data smoothing
- Gaussian kernel
Overview of algorithm
Sum of acceleration,
Overview of algorithmData segmentationPeak detection
Sum of accelerationHysteresis
threshold
Overview of algorithmFeatures
Manipulation
Linear acceleration
Wrist roll motion
Regularity of roll
Overview of algorithmNaive Bayes Classifier
Assign most probable class, ci in C
Given features f1,f2, …, fN
Feature probability
Data collectionCollected using iPhone 4
Programmable , large amount of memory, accelerometer and gyroscope
Recorded at 15Hz2 sets of data
Set 1: 20 recordingsSet 2: 23 recordings
A total of 449 hours of dataData training
5 minute non-EA segmentsFull segments for EA
Current work
Motivation: improve previous accuracy of 81%
Introduction of 3 new features:Regularity of manipulationTime since last EACumulative eating time
FeaturesFeature 1, regularity of manipulation
Regularity of peaks around 4000-5000 (deg/s)/G
Peaks every 10 – 30 seconds?
EA manipulation segment Non-EA manipulation segment
Regularity of manipulationSmooth manipulation data (N = 225, R =
37.6)Compute FFTCompute:
Units: (deg/s3)/G
Regularity of manipulationCalculate for each segment in dataDistribution statistics can be used for Bayes classifier
29>>
Distributions (set 1)
Regularity of manipulation
Distributions (set 2)
34>>
Features
Feature 2, time since last eating activityTime componentAfter a person eats, very unlikely to eat again
immediatelyProbability starts increasing as time passes
Time since last EALet tlast = end time of last segments classified
as EALet t = middle of time of unknown segment
currently being classifiedThen,
Time since last EABayes classifier requires probability
distributions for both EA and non-EAIt is possible to calculate time between mealsNonsensical for opposite class
Time since last non-EA?1 – p(f|EA)
Time since last EA Compute cumulative distribution function (CDF) of time since last
EA. p(f|EA) = CDF, p(f|nonEA) = 1 - CDF
CDF for time since last EA
(set 2)
Features
Feature 3, cumulative eating timeTime componentPeople spend a certain amount of time eating
and drinking in a day(Around 1.1 hrs. according to Dept. of Labor Statistics )
Cumulative eating timeAt time t, cumulative eating time:
Distribution of times involving non events are nonsensical
Compute CDF for each recording and average in each data set
Cumulative eating time
CDF for cumulative eating time (set 2)
Cumulative eating timep(f|EA) =
σ2cdf, μcdf from average CDF
p(f|nonEA) = 1 – p(f|EA)
Evaluation metricsOverall accuracy
EA accuracy
Non-EA accuracy
ResultsPrevious work
Statistics
Accuracy
ResultsRegularity of manipulation
Statistics
Accuracy
Regularity of manipulation (Results)
Standard deviation relatively large for EA distribution (<<18)
Set 1’s EA distribution non GaussianFFT not completely discriminating between
EAs and non-EAs
Regularity of manipulation (Results)
Smoothed manipulation segment from EA distribution (right tail)
Smoothed manipulation segment from non-EA distribution (left tail)
Regularity of manipulation (Results)Smoothed manipulation segment from EA distribution (middle)
Smoothed manipulation segment from non-EA distribution (middle)
<<20
Regularity of manipulation (Results)
Original data for segment in middle of EA distribution
Original data for segment in middle of non-EA distribution
ResultsTime since last EA
Statistics
AccuracySet 1 Set 2
Time since last EA (Results)
Original 4 featuresOriginal 4 features + time since last EA
Time since last EA (Results)
Original
Including time since last EA
• FPs are strong inhibitors for immediately subsequent data
ResultsCumulative eating time
Statistics
AccuracySet 1 Set 2
Cumulative eating time (Results)
Original 4 featuresOriginal 4 features + cumulative eating time
Cumulative eating time (Results)
Original
Including cumulative eating time
• FPs are strong inhibitors for immediately subsequent data
Conclusion
FFT not discriminating between EAs and non-EAs completely
Time-based features act as clocksFuture work
Explore regularity of manipulation using non-sinusoidal transform
Explore off-line analysis using time-based features so the optimal daily solution can be found (HMMs)
Questions?