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PHYSICAL ACTIVITY & HEALTH

•  Lack of physical activity is a major problem today – Epidemics quickly expanding

(hypertension, diabetes, etc.)

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PHYSICAL ACTIVITY & HEALTH

•  Lack of physical activity is a major problem today – Epidemics quickly expanding

(hypertension, diabetes, etc.)

•  Wearable technology & continuous monitoring: – Better understand relations between

physical activity and health – Drive behavioral change 3  

WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION

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WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION

WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION

WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION

Combined data streams •  Higher accuracy •  Detect activities •  Strong link between heart rate,

oxygen uptake and energy expenditure

WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION

PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure

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Energy Expenditure

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PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure

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PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure

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PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure Heart Rate

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PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure Heart Rate

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PHYSIOLOGY IS PERSON-SPECIFIC

Energy Expenditure Heart Rate

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PHYSIOLOGY IS PERSON-SPECIFIC

•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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RESEARCH QUESTIONS

•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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RESEARCH QUESTIONS

Current solutions: •  Population based models: everyone is

the same

•  Laboratory calibrations are performed to determine normalization parameters (e.g. running heart rate) and personalize models -  i.e. context-specific HR

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INDIVIDUAL DIFFERENCES IN PHYSIOLOGY

•  Use wearable sensors and machine learning methods to determine context

•  Use physiological data during specific contexts to predict normalization parameters and personalize EE models without laboratory calibrations

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OUR APPROACH

CONTEXT: LOW LEVEL ACTIVITIES

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Wearable sensors

(acceleration, heart rate)

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Wearable sensors

(acceleration, heart rate)

Supervised learning

(generalized linear models,

SVMs)

Activity type, walking speed

CONTEXT: LOW LEVEL ACTIVITIES

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Phones (GPS)

CONTEXT: LOCATIONS

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Phones (GPS) Unsupervised

methods (rules)

Important places

CONTEXT: LOCATIONS

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Low level activities, important

places

CONTEXT: HIGH LEVEL ACTIVITIES

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Low level activities, important

places

Unsupervised methods (topic

models)

High level activity

composites

CONTEXT: HIGH LEVEL ACTIVITIES

HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR

Activity type, walking speed,

daily routine

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Activity type, walking speed,

daily routine

Contextualized HR

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HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR

Activity type, walking speed,

daily routine

Contextualized HR

HR normalization

parameter

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Regression model

HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR

Heart Rate

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PHYSIOLOGY IS PERSON-SPECIFIC

Heart Rate Heart Rate Normalized

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PHYSIOLOGY IS PERSON-SPECIFIC

PHYSIOLOGY IS PERSON-SPECIFIC

Heart Rate Heart Rate Normalized

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dynamic

walking running biking

28% 33% 29% 3% 0.60

kcal/min 0.58

kcal/min 1.13

kcal/min 0.81

kcal/min 1.25

kcal/min 0.89

kcal/min 1.38

kcal/min 0.92

kcal/min

•  Reduces error up to 33% •  Does not require individual

calibration or laboratory recordings

RESEARCH QUESTIONS

•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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CARDIORESPIRATORY FITNESS ESTIMATION

•  Cardiorespiratory fitness is a widely used marker of overall health – Higher CRF showing lower risk of all cause

mortality

Current solutions: •  Maximal and submaximal tests: can be

risky for individuals in suboptimal health conditions, expensive, require medical supervision, laboratory equipment, spot measurement only

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Activity type, walking speed,

daily routine

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CRF ESTIMATION USING CONTEXT-SPECIFIC HR

Activity type, walking speed,

daily routine

Contextualized HR

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CRF ESTIMATION USING CONTEXT-SPECIFIC HR

HR  

CRF model

Activity type, walking speed,

daily routine

Contextualized HR

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CRF ESTIMATION USING CONTEXT-SPECIFIC HR

HR  

CRF model

Activity type, walking speed,

daily routine

Contextualized HR

CRF  

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CRF ESTIMATION USING CONTEXT-SPECIFIC HR

HR  

CRF model

Activity type, walking speed,

daily routine

Contextualized HR

CRF  

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•  10.3% error reduction when using low level context

•  22.6% error reduction when combining low and high level context

CRF ESTIMATION USING CONTEXT-SPECIFIC HR

RESEARCH QUESTIONS

•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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RESEARCH QUESTIONS

•  How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level?

•  Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status?

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RESEARCH QUESTIONS

CRF  

HR  

42  CRF model

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

CRF  

EE model

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CRF  

HR  

CRF model

EE  

HR  ACC  

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

CRF  

EE model

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CRF  

HR  

CRF model

EE  

HR  ACC  

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

CRF  

EE model

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CRF  

HR  

CRF model

EE  

HR  ACC  

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

CRF  

EE model

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CRF  

HR  

CRF model

EE  

HR  ACC  

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS

CRF  

EE model

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CRF  

HR  

CRF model

EE  

HR  ACC  

•  No need for explicit HR normalization

•  RMSE reduced by 18% on average

CONCLUSIONS

•  We personalized EE estimation models without the need for individual calibration in laboratory settings –  reduced RMSE up to 33% (HR

normalization and hierarchical modeling)

•  We proposed new methods for context recognition and CRF estimation in free-living without requiring laboratory tests –  reduced CRF estimation error up to 22.6%

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