1.
Recognition of Human Physical
Activity based on a novel
Hierarchical Weighted Classification
scheme
IJCNN 2011
San José, California (USA)
O. Banos, M. Damas, H. Pomares, I. Rojas
Department of Computer Architecture and
Computer Technology (University of Granada)
Work supported in part by the Spanish CICYT Project TIN2007-60587,
Junta de Andalucia Projects P07-TIC-02768 and P07-TIC-02906, the
CENIT project AmIVital and the FPU Spanish grant AP2009-2244

2.
Agenda
Introduction
Experimental setup
Methods
Results
Conclusions
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3.
Activity Recognition
• Fundamental part of medical/health assistant
work, being applicable to other areas (sport
efficiency, videogames industry, robotics, etc.)
• Changeableness due to the capability to discover
and identify actions, movements and gestures
than normally are unnoticed
• Objectives
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 Define an original methodology
 Identify the main characteristics
 Improve state of the art results through efficient
and accurate knowledge inference algorithms

4.
Experimental setup
Fiveaccelerometers
Walking
Sitting and
relaxing
Standing still
Running
Bicycling
Lying down
Brushing teeth
Climbing stairs
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Eightactivities

5.
Data preprocessing
• Different approaches were studied
• Best results “a posteriori” using a LPF+HPF (IIR
elliptic)
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ORIGINAL MEAN FILTERING LPF+HPF

6.
Feature extraction
Magnitudes
Amplitude
Autocorrelation function
Cepstrum
Correlation lags
Cross correlation function
Energy spectral density
Spectral coherence
Spectrum amplitude/phase
Histogram
Historical data lags
Minimum phase reconstruction
Wavelet decomposition
Statistical functions
4th and 5th central statistical moments
Energy
Arithmetic/Harmonic/Geometric/ Trimmed mean
Entropy
Fisher asymmetry coefficient
Maximum / Position of
Median
Minimum / Position of
Mode
Kurtosis
Data range
Total harmonic deviation
Variance
Zero crossing counts
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7.
Why feature selection is needed?
• Influence on classification process
OPTIMUM
Few Features
Good classification
0 500 1000
-1
-0.5
0
0.5
1
x 10
4 Thigh accelerometer
Features
Featurevalue
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• Huge feature set (861 parameters 
2861  1.5 x 10259 possible combinations)
Mann-Whitney-
Wilcoxon FS

8.
Binary vs. multiclass classification
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• # of classes discriminated
• Binary classifiers are more accurate in general
than direct multiclass classifiers
• Problem: define an adequate multiclass
extension scheme
• Depends on the particular experiment
• No general models for multisource problems
2  SVM, NB
≥2  DT

9.
Hierarchical weighted classifier (HWC)
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10.
Results
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• Naïve Bayes
• 10-fold cross validation
• N=8, M=5
70,00
80,00
90,00
100,00
Hip Wrist Arm Ankle Thigh Fusion
Classificationaccuracy(%)
Using 1 feature for eachclass classifier
MV
HWC
70,00
80,00
90,00
100,00
Hip Wrist Arm Ankle Thigh Fusion
Classificationaccuracy(%)
Using 10 features for eachclass classifier
MV
HWC

11.
Comparison with other studies
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Work Accuracy rates
S.W. Lee and K. Mase. Activity and location
recognition using wearable sensors. 92.85% to 95.91%
J. Mantyjarvi, J. Himberg, and T. Seppanen.
Recognizing human motion with
multiple acceleration sensors.
83.00% to 90.00%
K. Aminian, P. Robert, E. E. Buchser,
B. Rutschmann, D. Hayoz, and M. Depairon.
Physical activity monitoring based on accelerometry:
validation and comparison with video observation.
89.30%
L. Bao and S.S. Intille. Physical Activity Recognition
from Acceleration Data under Semi-Naturalistic
Conditions
89.00%
THIS WORK 97.08% (1 feat.), 97.81% (10 feat.)
Source: L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions

12.
Conclusions
• The hierarchical system defined only requires:
▫ Binary classifiers based on individual features with
high binary discriminant capability
▫ A few weighting parameters and simple decision rules
• According to the particular activity recognition
model:
▫ HWC offers better results for both source and fusion
classification approaches
 Improvement up to 15% with respect to MV
 Similar good results when fusion for both 1 and 10
features are used
 Particular good results for the wrist or arm data based
source classifier (≈96%)
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13.
Future work
• Test non-linear subclassifiers combinations
• Include a probability of membership to the
different classes besides the current weighted
scheme
• Analyze other multiclass extensions and
compare with HWC performance
• Spread the study to a larger number of activities
(classes)
• Apply this methodology to other kind of
problems
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14.
Thank you for your attention
Questions?
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Oresti Baños Legrán
Dep. Computer Architecture & Computer Technology
Faculty of Computer & Electrical Engineering (ETSIIT)
University of Granada, Granada (SPAIN)
Email: oresti@atc.ugr.es, oresti@ugr.es
Phone: +34 958 241 516
Fax: +34 958 248 993