Presentation for EMBC 2013 — Wearable sensors have great potential for accurate estimation of Energy Expenditure (EE) in daily life. Advances in wearable technology (miniaturization, lower costs), and machine learning techniques as well as recently developed self-monitoring movements, such as the Quantified Self, are facilitating mass adoption. However, EE estimations are affected by a person’s body weight (BW). BW is a confounding variable preventing meaningful individual and group comparisons. In this paper we present a machine learning approach for BW normalization and activities clustering. In our approach to activity-specific EE modeling, we adopt a genetic algorithm- based clustering scheme, not only based on accelerometer (ACC) features, but also on allometric coefficients derived from 19 subjects performing a wide set of lifestyle and gym activities. We show that our approach supports making comparisons be- tween individuals performing the same activities independently of BW, while maintaining accuracy in the EE estimate.
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Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering
1. Body Weight-Normalized Energy Expenditure
Estimation using Combined Activity and
Allometric Scaling Clustering
Marco Altini, Julien Penders, Oliver Amft
2. Physical Activity Monitoring
-> Energy Expenditure
- Basal Metabolic Rate (BMR)
- Diet Induced Thermogenesis (DIT)
- Physical Activity Energy Expenditure (PAEE)
BMR BMR
DIT
DIT
PAEE PAEEinactive active
5. Energy Expenditure, Health and Body
Weight
- Quantify Energy Expenditure
- > Understand relation between Physical Activity
and Health (e.g. How much activity do we need?)
Need for Normalization
7. Allometric Modeling -Tool#2
-> Relationship between body size and physiology
Power law
– y = EE, x = BW, k = constant
– If β = 1, classic normalization
• No optimal single coefficient
– Coefficients are activity-dependent
y = k X
-β
8. Toolbox Summary
• Activity-Specific Energy Expenditure models
• Allometric modeling
-> Combined these methods to normalize EE
1. What allometric coefficients to use?
2. How to group activities taking into account:
• Activity recognition task
• Allometric coefficients
12. biking 60 rpm lev high
biking 60 rpm lev low
biking 60 rpm lev med
biking 80 rpm lev high
biking 80 rpm lev low
biking 80 rpm lev med
cleaning table
cleaning windows
cooking
folding clothes
lying
moving boxes
PC work
reading
running 10 km/h
running 7 km/h
running 8 km/h
running 9 km/h
sitting
sitting desk work
stacking groceries
standing
vacuuming
walk carrying 4 kg
walking 3 km/h
walking 3 km/h 10% inc
walking 3 km/h 5% inc
walking 4 km/h
walking 5 km/h
walking 5 km/h 10% inc
walking 5 km/h 5% inc
walking 6 km/h
walking self-paced
washing dishes
watch TV
writing
1) What Allometric Coefficients?
13. 2) How To Group Activities?
• Multi-Objective Optimization Problem
– Grouping 48 activities into clusters according to
two criteria:
• Activity-Specific allometric coefficients
• Practical Activity Recognition
-> Unsupervised Clustering
– Genetic Algorithm (optimal k-means clustering)
– Features: signal power, motion intensity, β
15. EE Algorithm Implementation
Accelerometer
Features (time and
frequency domain)
Activity Recognition
SVM, distinguishes 5
clusters of activities
Cluster 1 Model
Cluster 4 Model
Energy
Expenditure
Heart Rate
Cluster 2 Model
Cluster 3 Model
Cluster 5 Model
95.1%
accuracy
1.05 kcal/min
RMSE
16. Evaluation
sitting
EEkcal/min
0.00.40.81.2
walking 5 km/h
0123456
biking 80 rpm
02468
running 10 km/h
0246812
EEkcal/min/kg
0.00000.00100.00200.0030
0.00000.00100.0020
0.000.020.040.060.08
0.0000.0020.0040.006
subj 8 subj 18
EEkcal/min/BWb
0.000.050.100.15
subj 8 subj 18
0.000.040.080.12
subj 8 subj 18
01234
subj 8 subj 18
0.000.100.200.30
No Normalization
• Prevents comparisons between groups and
individuals
• Prevents comparison within individuals
undergoing weight changes
17. Evaluation
sitting
EEkcal/min
0.00.40.81.2
walking 5 km/h
0123456
biking 80 rpm
02468
running 10 km/h
0246812
EEkcal/min/kg
0.00000.00100.00200.0030
0.00000.00100.0020
0.000.020.040.060.08
0.0000.0020.0040.006
subj 8 subj 18
EEkcal/min/BWb
0.000.050.100.15
subj 8 subj 18
0.000.040.080.12
subj 8 subj 18
01234
subj 8 subj 18
0.000.100.200.30
Simple Ratio between EE and BW (e.g. kcal/kg)
• Overcorrects
• Doesn’t capture activity-dependence
19. Summary and Conclusions
• Energy Expenditure
– Objective quantification of Physical Activity
• Normalization
– Allometric coefficients
– Activity recognition feasibility
• New Opportunities for
– Comparisons between groups and individuals
– Comparison within individuals undergoing weight
changes
20. Body Weight-Normalized Energy Expenditure
Estimation using Combined Activity and
Allometric Scaling Clustering
Marco Altini, Julien Penders, Oliver Amft
Thank You