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?