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)

1. Novel Method for Feature-set
Ranking Applied to Physical Activity
Recognition
IEA-AIE 2010
Córdoba (SPAIN)
O. Baños, H. Pomares, I. Rojas

2. 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

3. 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

4. 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
4
 Define an original methodology
 Identify the main characteristics
 Improve results in unsupervised monitoring studies

5. Experimental setup
• Five accelerometers
Walking
Sitting and
relaxing
Standing
still
Running
5
• Four activities
• Twenty subjects • Two monitoring methodologies

6. Data preprocessing
• Different approximations were studied
• Best results “a posteriori” using a LPF+HPF (IIR
elliptic)
6
ORIGINAL MEAN FILTERING LPF+HPF

7. 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
7

8. 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
1
x 10
4 Thigh accelerometer
Features
Featurevalue
8
• Huge feature set (861 parameters 
2861  1.5 x 10259 possible combinations)

9. Feature selection
0
5
10
15
20
25
30
Wavelet coef. (a5) geometric mean
Featurevalue
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 2
1
1.5
2
2.5
3
3.5
4
Walking
Sitting and relaxing
Standing still
Running
9

10. Feature selection
0 0.2 0.4 0.6 0.8 1
0
200
400
600
800
1000
Overlapping Threshold
No.DiscriminantFeatures
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
100
200
300
400
500
600
700
800
900
Overlapping Threshold
No.DiscriminantFeatures
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
okpifkclassdiscrim.no
okpifkclassdiscrim.
f
)(
)(


11. Feature selection
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
0
100
200
300
400
500
600
700
800
900
Overlapping Threshold
No.DiscriminantFeatures
Walking
Sitting and relaxing
Standing still
Running
All activities
All activities & all accelerometers
0 0.2 0.4 0.6 0.8 1
0
200
400
600
800
1000
Overlapping Threshold
No.DicriminantFeatures
11
• Features extraction based on a
windowing method
• Data corresponding to hip
accelerometer





thf
thf
okpifkclassdiscrim.no
okpifkclassdiscrim.
f
)(
)(


12. Classification (SVM)
12
• Fast
• Simple solutions
• Good precedents
• Binary multiclass
models based on
• Different kernels
(linear, quadratic,
RBF, MPL, etc.)

13. Classification (SVM)
13
• Fast
• Simple solutions
• Good precedents
• Binary multiclass
models based on
• Different kernels
(linear, quadratic,
RBF, MPL, etc.)

14. Classification (DT)
14
• Very fast
• Easy
interpretability
• Entropy related

15. Test
15
• 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 (%)

16. Comparison with other studies
16
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

17. 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%)
17
Filtering
Feature
extraction over
the complete
signal
Features selected:
coef. wavelets,
autocorrelación or
amplitude
geometric mean
Classification
based on DT

18. 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,…)
18

19. Thank you for your attention
Questions?
19