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
4
 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
7

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)

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

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
)(
)(


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
)(
)(


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

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

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

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 (%)

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

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

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

Thank you for your attention
Questions?
19