Sensor Fusion for Energy Expenditure Estimation · Sensor Fusion for Energy Expenditure Estimation...

Sensor Fusion for Energy Expenditure Estimation

Dominik Schuldhaus1, Sabrina Dorn1, Heike Leutheuser1, Alexander Tallner2,

Jochen Klucken3, Bjoern M. Eskofier1

December 4, 2013

1Digital Sports Group, Pattern Recognition Lab, University of Erlangen-Nuremberg, Germany

2Institute of Sport Science and Sport, University Erlangen-Nuremberg, Germany

3Department of Molecular Neurology, University Hospital Erlangen, Germany

December 4, 2013 | Dominik Schuldhaus | University of Erlangen-Nuremberg | Decision Level Fusion 2

World Health Organization

Overweight and obesity:

fifth leading risk for global deaths

[http://www.foxnews.com]

December 4, 2013 | Dominik Schuldhaus | University of Erlangen-Nuremberg | Decision Level Fusion 3

Physical Activity (PA)

0

50

100

%

Active

Not active

Active State Distribution

Assessment of PA

December 4, 2013 | Dominik Schuldhaus | University of Erlangen-Nuremberg | Decision Level Fusion

Self reports

Energy Expenditure

4

Assessment of PA

[www.mojolondon.co.uk]

Energy Expenditure Estimation

in

Daily Life


State-of-the-Art Algorithms

5

Sensor

Literature

• 1994: Bouten et al.

• 2010: Vathsangam et al.

• 2012: Liu et al.

Small and lightweight sensors

• Fusion of sensor data

Estimation of energy expenditure

• Daily life activities

• Running on treadmill


[Hall et al. 1997]

Feature Level Fusion

6

Feature Extraction

Regression

Preprocessing Preprocessing Preprocessing

Sensor 1 Sensor 2 Sensor 3


[Hall et al. 1997]


7

Feature Extraction

Regression

Preprocessing Preprocessing Preprocessing Preprocessing

Sensor 1 Sensor 2 Sensor 3 Sensor 4

Major need:

No retraining of system

after adding sensors


[Hall et al. 1997]


8

Feature Extraction

Regression


Sensor 1 Sensor 2 Sensor 3 Sensor 4

Major need:


after adding sensors

Decision level fusion


Study: Data Collection

9

# Participants 10

# Male 7

Age [years] 49 ± 12

Height [cm] 178 ± 10

Weight [kg] 80.7 ± 14.6

EnEx database:

http://www.activitynet.org

http://www.activitynet.org/


Study: Sensor Setup

10

SHIMMER 3-D accelerometer 1.5 g (hip)

6.0 g (ankle)

3-D gyroscope 500 °/s (hip)

2000 °/s (ankle)

Sampling rate 204.8 Hz


Study: Exercises

11

Traditional

• 3.2 km/h

• 4.8 km/h

• 6.4 km/h

Oscillating

• 3.2 km/h

• 4.8 km/h

• 6.4 km/h

• Each speed level: 6 min

• Treadmill


Reference System

12

Spirometry system

Metabolic equivalent

(MET)


Study: Example Data

13

• Treadmill - traditional

• Three speed levels

• Angular velocity in sagittal plane (ankle)


Study: Example Data (2)

14

3.2 km/h 4.8 km/h

6.4 km/h


Proposed System

15

Decision Level Fusion


HP_ACC HP_GYR AK_ACC AK_GYR

Feature Extraction Feature Extraction Feature Extraction Feature Extraction

Regression Regression Regression Regression

HP: hip - AK: ankle - ACC: accelerometer - GYR: gyroscope


Preprocessing

16

Non-overlapping sliding windows: 30 sec

1.

2.

3.

Steady state segmentation: last three minutes


Feature Extraction

17

Features

Extrema

• Minimum

• Maximum

Statistical

• Mean of abs. amplitudes

• Standard deviation

• 10 / 25 / 50 / 75 / 90 th percentile

9 / axis

x

(3 axes accel or 3 axes gyro)

=

27 / sensor type


Regression

18

Comparison of algorithms

• Support Vector Regression (SVR)

• Classification and Regression Trees (CART)

• Multiple Linear Regression (MLR)

Performance assessment

• Mean absolute error [MET]

• Leave-one-subject-out cross-validation


Proposed System

19

Decision Level Fusion



Feature Extraction Feature Extraction Feature Extraction Feature Extraction

Regression Regression Regression Regression



Proposed System

20



MET MET MET MET

Mean

MET

Only adjustment after adding sensors:

Mean function



Results

21

Algorithm HP_ACC HP_GYR AK_ACC AK_GYR

SVR 0.64 ± 0.45 0.79 ± 0.71 0.71 ± 0.53 0.61 ± 0.41

CART 0.61 ± 0.36 0.96 ± 0.72 0.71 ± 0.51 0.67 ± 0.50

MLR 0.77 ± 0.58 0.79 ± 0.71 0.66 ± 0.48 0.85 ± 0.46

Mean absolute error [MET]

Decision level fusion

0.50 ± 0.13

Performance improvement by 18 %


Summary

22

0

50

100

Mon Tue Wed

% Active

Not active

Active State Distribution

• Sensor-based energy expenditure estimation

• Decision level fusion

• Mean absolute error: 0.5 MET

• No retraining of system after adding sensors


Outlook

23

• Comparison of fusion algorithms

• Adding physiological sensors

• Testing on daily life activities


Outlook: Benchmark Dataset

24

• Comparison of algorithms difficult:

No common used benchmark dataset

• http://www.activitynet.org

http://www.activitynet.org/

Thank you for your attention!

Bavarian Ministry of

Economic Affairs,

Infrastructure, Transport and

Technology

Sensor Fusion for Energy Expenditure Estimation · Sensor Fusion for Energy Expenditure Estimation...

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