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DeustoTech - Deusto Institute of Technology, University of Deustohttp://www.morelab.deusto.es
December 2, 2015
Facing up social activity recognition using smartphone sensors
Pablo Curiel, Ivan Pretel, Ana B. Lago
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Outline
Introduction
System Design
Evaluation
Conclusion
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Introduction
System Design
Evaluation
Conclusion
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Introduction
Introduction
AT HOME
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Introduction
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Introduction
► Location-based services► Foursquare, Twitter, Google Keep,…
► Low-level inference► Physical activity: walking, running,
cycling,…► High-level inference
► High-level user activities: cooking, reading novel,…
► Environments or surroundings: home, bar, public transport
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Introduction
► Socialization as a high-level user activity► based on environment recognition► provides “social reminders”
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Introduction
System Design
Evaluation
Conclusion
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System Design: Context capture
► Environments► Bar, café, sports bar, disco and restaurant
► Characteristics► Noisy places► Stationary positions► Artificially lighted places
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System Design: Context capture
► Captured Data► Audio
►RMS power and dBs►Microphone
► Acceleration►3-axial acceleration►Acceleration,
gyroscope and geomagnetic sensors
► Ambient luminosity►Luxes►Luminosity sensor.
► Screen status
► Used devices► LG Nexus 4 (100
hours)► HTC Desire 816 (20
hours)
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Data processing
► 3 steps► 1. Data fusion► 2. Data transformation► 3. Feature extraction
1. Data Fusion2. Data transformation3. Feature extraction
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Data processing
► 1. Data fusion► Timestamps► Gathering halts► Sample rate
►50Hz, 20Hz, 10Hz, 5Hz, 2Hz and 1Hz
1. Data Fusion2. Data transformation3. Feature extractionRMS, dBs
Acceleration, gyroscope, compassLuminosity, screen
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Data processing
► 1. Data fusion► 2. Data transformation
► Raw to processed characteristics1. Data Fusion2. Data transformation3. Feature extractionRMS, dBs
Acceleration, gyroscope, compassLuminosity, screen
LPF(RMS), LPF(dBs)Lineal-acc., earth-acc.log(lum), fixedLum, log(fixedLum)
+
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Data processing
► 1. Data fusion► 2. Data transformation► 3. Feature extraction
1. Data Fusion2. Data transformation3. Feature extractionRMS, dBs
Acceleration, gyroscope, compassLuminosity, screen
Max, min, mean, median, standard deviation LPF(RMS),
LPF(dBs)Lineal-acc., earth-acc.log(lum), fixedLum, log(fixedLum)
+
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Introduction
System Design
Evaluation
Conclusion
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Evaluation
► Training Set► 10x5-fold cross
validation► Nexus 4 70h
► Test Set► Nexus 4 30h► HTC Desire 20h
► Classifiers► Random forest► Support vector
machine (SVM) -Gaussian radial basis function kernel
► k-Nearest Neighbours (k-NN)
► Naive Bayes classifier
► Parameters► The best features to
use► The most suitable
window sizes► Classifier comparison► Sensor sampling rate
comparison► Performance
► Recall► Specificity► AUC► Accuracy
What is the best combination of parameters to detect bar-like environments?
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Evaluation
► Feature comparison► Acceleration features comparison
►Vector norm -> Random forest, SVM and k-NN leads to better results
►Types of acceleration– Linear = “Earth-acceleration” – Base acc. better than Linear&“Earth-acceleration”
(Random forest and SVM, 4%)► Audio features comparison
►dB better than RMS:– SVM(4% - 9%), k-NN(6% - 15%), Naive Bayes (2% -
8%)►Filtered better than Unfiltered (k-NN is the only
exception)► Luminosity features comparison
►Combination of log transformation and the fixed version is the best choice– Random Forest (1%), SVM(3%), k-NN(-), Naive
Bayes (11%)
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Evaluation
► Contribution of each sensor► Training with the best performing feature of each
sensor ►concluded in the previous comparisons
► Results►Audio exclusion declines from 15% to 20%►Acceleration exclusion declines from 1% to 10%►Luminosity only useful for SVM and Naive Bayes
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Evaluation
► Window size comparison► Common pattern: The smaller the window size, the
worse the results► Random Forest
►240 seconds ►120 or 90 -> 2% performance lost
► SVM►120 seconds►60 seconds -> 2% performance lost
►k-NN classifier►180 seconds ►60 seconds-> less than 2% performance lost
►Naive Bayes►240 seconds ►120 seconds -> 2% performance lost
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Evaluation
► Sample rate comparison►Smaller window sizes suffer more than bigger
ones when this parameter is decreased
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Evaluation
► Classifier comparison►The best is SVM
►+ recall►+ AUC►+ accuracy
► Random Forest►+ specificity
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Evaluation
► The best performing configuration► SVM► Features
►Linear acceleration
►Filtered dBs►Log-
transformed fixed luminosity
► Capable of generalizing to new environments►User and
device dependencies
Bar-like TPFN
Other FPTN
Bar-like TPFN
Other FPTN
► Results
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Introduction
System Design
Evaluation
Conclusion
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Conclusion
► Findings► The preliminary results obtained seem promising
regarding the recognition of new locations for the same user.
► However, generalization to new users seems to be more troublesome.
► Future work► New data collection campaign which involves more
users in order to better study these aspects►Study what is the most descriptive value for
each feature (mean, median, standard deviation, minimum and maximum)
► Search for better recognition results with separate classes for each type of bar-like environment, as this could potentially enable to better capture the particular characteristics each of these environments has.
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Thank you for your attention
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DeustoTech - Deusto Institute of Technology, University of Deustohttp://www.morelab.deusto.es
Facing up social activity recognition using smartphone sensors
Pablo Curiel, Ivan Pretel, Ana B. Lago{[email protected]} {[email protected]} {[email protected]}
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