novel method for feature-set ranking applied to physical activity recognition
DESCRIPTION
The benefits arising from proactive conduct and subject-specialized healthcare have driven e-health and e-monitoring into the forefront of research, in which the recognition of motion, postures and physical exercise is one of the main subjects. We propose here a multidisciplinary method for the recognition of physical activity with the emphasis on feature extraction and selection processes, which are considered to be the most critical stages in identifying the main unknown activity discriminant elements. Efficient feature selection processes are particularly necessary when dealing with huge training datasets in a multidimensional space, where conventional feature selection procedures based on wrapper methods or ‘branch and bound’ are highly expensive in computational terms. We propose an alternative filter method using a feature quality group ranking via a couple of two statistical criteria. Satisfactory results are achieved in both laboratory and semi-naturalistic activity living datasets for real problems using several classification models, thus proving that any body sensor location can be suitable to define a simple one feature-based recognition system, with particularly remarkable accuracy and applicability in the case of the wrist. This presentation illustrates part of the work described in the following articles: * Banos, O., Damas, M., Pomares, H., Prieto, A., Rojas, I.: Daily Living Activity Recognition based on Statistical Feature Quality Group Selection. Expert Systems with Applications, vol. 39, no. 9, pp. 8013-8021 (2012) * Banos, O., Pomares, H., Rojas, I.: Ambient Living Activity Recognition based on Feature-set Ranking Using Intelligent Systems. In: Proceedings of the 2010 International Joint Conference on Neural Networks (IJCNN 2010), IEEE, Barcelona, July 18-23, (2010)TRANSCRIPT
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”
2
• 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
3
• 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
5
• Four activities
• Twenty subjects
• Two monitoring methodologies
Data preprocessing
• Different approximations were studied
• Best results “a posteriori” using a LPF+HPF (IIR elliptic)
6
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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