Novel Method for Feature-set
Ranking Applied to Physical Activity
Recognition
IEA-AIE 2010
Córdoba (SPAIN)
O. Baños, H. Pomares, I. Rojas
Health Sector Today
• Innovations in Technology and Globalization have transformed health services
• Medical interventions have changed from “direct and specific person treatment” to “continuous and spatio-independent interaction”
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• Acute diseases have evolved to chronic diseases, while World population is becoming older
AmiVital Project
• Create an integral and consistent approach for the provision of AmI (Ambient Intelligence) services to citizens, from both a social and health care perspective
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• Merge concepts from the AmI paradigm and the current framework for health assistance into a more general and integral model of services
Activity Recognition
• Fundamental part of medical/health assistant work, being applicable to other areas (sport efficiency, videogames industry, robotics, etc.)
• Changeableness due to capability for discovering and identifying actions, movements and gestures than normally are unnoticed
• Objectives
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Define an original methodology Identify the main characteristics Improve results in unsupervised monitoring studies
Experimental setup • Five accelerometers
Walking Sitting and relaxing Standing still Running
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• Four activities
• Twenty subjects
• Two monitoring methodologies
Data preprocessing
• Different approximations were studied
• Best results “a posteriori” using a LPF+HPF (IIR elliptic)
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ORIGINAL MEAN FILTERING LPF+HPF
Feature extraction
Magnitudes
Amplitude Autocorrelation Cepstrum Correlation lags Cross correlation 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 Standard deviation Total harmonic deviation Variance Zero crossing counts
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0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 21
1.5
2
2.5
3
3.5
4
Walking
Sitting and relaxing
Standing still
Running
Why feature selection is needed?
• Influence on classification process
OPTIMUM
Few Features Good classification
0 500 1000-1
-0.5
0
0.5
1x 10
4 Thigh accelerometer
Features
Fe
atu
re v
alu
e8
• Huge feature set (861 parameters 2861 1.5 x 10259 possible combinations)
Feature selection
0
5
10
15
20
25
30
Wavelet coef. (a5) geometric mean
Fe
atu
re v
alu
e
Discriminant
capacity Robustness
Quality
group
4 5 1
4 4 2
4 3 3
4 2 4
4 1 5
3 5 6
3 4 7
3 3 8
3 2 9
3 1 10
2 5 11
2 4 12
2 3 13
2 2 14
2 1 15
1 5 16
1 4 17
1 3 18
1 2 19
1 1 20
0 5 21
Overlapping criteria
Robustness criteria
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 21
1.5
2
2.5
3
3.5
4
Walking
Sitting and relaxing
Standing still
Running
9
Feature selection
0 0.2 0.4 0.6 0.8 10
200
400
600
800
1000
Overlapping Threshold
No
. D
iscri
min
an
t F
ea
ture
s
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
100
200
300
400
500
600
700
800
900
Overlapping Threshold
No
. D
iscri
min
an
t F
ea
ture
s
Walking
Sitting and relaxing
Standing still
Running
All activities
All activities & all accelerometers
10
• Features extracted from the complete signal • Data corresponding to hip accelerometer
thf
thf
okpifk class discrim. no
okpifk class discrim.f
)(
)(
Feature selection
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
100
200
300
400
500
600
700
800
900
Overlapping Threshold
No
. D
iscri
min
an
t F
ea
ture
s
Walking
Sitting and relaxing
Standing still
Running
All activities
All activities & all accelerometers
0 0.2 0.4 0.6 0.8 10
200
400
600
800
1000
Overlapping Threshold
No
. D
icri
min
an
t F
ea
ture
s
11
• Features extraction based on a windowing method • Data corresponding to hip accelerometer
thf
thf
okpifk class discrim. no
okpifk class discrim.f
)(
)(
Classification (SVM)
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• Fast
• Simple solutions
• Good precedents
• Binary multiclass models based on
• Different kernels (linear, quadratic, RBF, MPL, etc.)
Classification (SVM)
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• Fast
• Simple solutions
• Good precedents
• Binary multiclass models based on
• Different kernels (linear, quadratic, RBF, MPL, etc.)
Classification (DT)
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• Very fast
• Easy interpretability
• Entropy related
Test
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• Cross validation
▫ Leave-one-subject-out
▫ 50% training – 50% test
SVM DT
LAB 96.37 ± 4.58 98.92 ± 1.08
SEM 75.81 ± 0.90 95.05 ± 1.20
Mean (%) ± standard deviation (%)
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% a 95.91%
J. Mantyjarvi, J. Himberg, and T. Seppanen. Recognizing human motion with multiple acceleration sensors.
83% a 90%
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%
THIS WORK 95.05% (SEM), 98.92(LAB)
Source: L. Bao and S.S. Intille. Physical Activity Recognition from Acceleration Data under Semi-Naturalistic Conditions
Conclusion
• Only a source of data (accelerometer ) is necessary for inferring the considered activities
• Best results (≈ 100%) for laboratory data:
• Seminaturalistic accuracy rates are highly improved with respect to prior works (≈ 95%)
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Filtering
Feature extraction over
the complete signal
Features selected: coef. wavelets,
autocorrelación or amplitude
geometric mean
Classification based on DT
Future work
• Analyze other methods and compare with the presented work
• Study other activities and apply this methodology to other kind of problems
• Define new approaches for other physiological parameters (ECG, PPG, body temperature,…)
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Thank you for your attention
Questions?
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