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

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)

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

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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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