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Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering

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Body Weight-Normalized Energy Expenditure Estimation Using Combined Activity and Allometric Scaling Clustering

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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.

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. 1. Body Weight-Normalized Energy Expenditure Estimation using Combined Activity and Allometric Scaling Clustering Marco Altini, Julien Penders, Oliver Amft
  2. 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
  3. 3. time (minutes) 0 20 40 60 51015 time (minutes) 0 20 40 60 0.00.51.01.5 0 20 40 60 6080100120140 Walking biking Running Sedentary Household MotionintensityEnergyExpenditure EE ACC HeartRate Time (minutes) HR Walking biking Running Sedentary Household Walking biking Running Sedentary Household
  4. 4. Accelerometer Features Activity Recognition Anthropometric Characteristics (e.g. Body Weight) Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models – Tool#1
  5. 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
  6. 6. 50 60 70 80 90 100 1012141618 Running Body Weight (kg) EE(kcal/min) r= 0.86 * 50 60 70 80 90 100 5.05.56.06.57.07.5 Biking Body Weight (kg) EE(kcal/min) r= 0.29 50 60 70 80 3456 Walking Body Weight EE(kcal/min) r= 0.72 * 50 60 70 80 90 100 9.09.510.010.511.011.5 Body Weight (kg) EE(METs) r= -0.48 * 50 60 70 80 90 100 4567 Body Weight (kg) EE(METs) r= -0.81 * 50 60 70 80 2.53.03.54.04.5 Body Weight EE(METs) r= -0.08 How To Normalize? 50 60 70 80 90 100 1012141618 Running Body Weight (kg) EE(kcal/min) r= 0.86 * 50 60 70 80 90 100 5.05.56.06.57.07.5 Biking Body Weight (kg) EE(kcal/min) r= 0.29 50 60 70 80 3456 Walking Body Weight EE(kcal/min) r= 0.72 * 50 60 70 80 90 100 9.09.510.010.511.011.5 Body Weight (kg) EE(METs) r= -0.48 * 50 60 70 80 90 100 4567 Body Weight (kg) EE(METs) r= -0.81 * 50 60 70 80 2.53.03.54.04.5 Body Weight EE(METs) r= -0.08 Body Weight Body Weight
  7. 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. 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
  9. 9. 9 O2 CO2 Indirect Calorimeter
  10. 10. 10 ECG Necklace ACC HR
  11. 11. 11 19 Subjects, 48 Activities Household Sport
  12. 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. 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
  14. 14. Clustering Output -3 -2 -1 0 1 2 -1.5-1.0-0.50.00.51.01.52.0 -1 0 1 2 3 normalzied allometric coefficient normalizedMI normaizedPow cluster 1 cluster 2 cluster 3 cluster 4 cluster 5 running walking biking 0.05 0.80 0.7 0.55 0.99
  15. 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. 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. 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
  18. 18. 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
  19. 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. 20. Body Weight-Normalized Energy Expenditure Estimation using Combined Activity and Allometric Scaling Clustering Marco Altini, Julien Penders, Oliver Amft Thank You

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