We introduce an approach to personalize energy expenditure (EE) estimates in free living. First w... more We introduce an approach to personalize energy expenditure (EE) estimates in free living. First we use Topic Models (TM) to discover activity composites from recognized activity primitives and stay regions in daily living data. Subsequently, we determine activity composites that are relevant to contextualize heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activityspecific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including 34 healthy volunteers, we show that HR normalization in 14-day free living data improves accuracy compared to no normalization and normalization based on activity primitives only (29.4% and 19.8% error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by 10.7% in a leave-oneparticipant-out analysis.
Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key eleme... more Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration. Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual calibration or explicit heart rate normalization since CRF accounts for the different relation between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate during low intensity activities of daily living, showing that CRF can be determined without specific protocols. Reference VO2max and EE were collected on a sample of 32 participants with varying CRF level. CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide evidence that hierarchical modeling is a promising technique for generalized CRF estimation from activities of daily living and personalized EE estimation.
This article is part of the focus theme of Methods of Information in Medicine on "Pervasive ... more This article is part of the focus theme of Methods of Information in Medicine on "Pervasive Intelligent Technologies for Health". Energy Expenditure (EE) estimation algorithms using Heart Rate (HR) or a combination of accelerometer and HR data suffer from large error due to inter-person differences in the relation between HR and EE. We recently introduced a methodology to reduce inter-person differences by predicting a HR normalization parameter during low intensity Activities of Daily Living (ADLs). By using the HR normalization, EE estimation performance was improved, but conditions for performing the normalization automatically in daily life need further analysis. Sedentary lifestyle of many people in western societies urge for an in-depth analysis of the specific ADLs and HR features used to perform HR normalization, and their effects on EE estimation accuracy in participants with varying Physical Activity Levels (PALs). To determine 1) which low intensity ADLs and HR ...
We introduce an approach to personalize energy expenditure (EE) estimates in free living. First w... more We introduce an approach to personalize energy expenditure (EE) estimates in free living. First we use Topic Models (TM) to discover activity composites from recognized activity primitives and stay regions in daily living data. Subsequently, we determine activity composites that are relevant to contextualize heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activityspecific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including 34 healthy volunteers, we show that HR normalization in 14-day free living data improves accuracy compared to no normalization and normalization based on activity primitives only (29.4% and 19.8% error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by 10.7% in a leave-oneparticipant-out analysis.
Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key eleme... more Accurate estimation of energy expenditure (EE) and cardiorespiratory fitness (CRF) is a key element in determining the causal relation between aspects of human behavior related to physical activity and health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration. Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual calibration or explicit heart rate normalization since CRF accounts for the different relation between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate during low intensity activities of daily living, showing that CRF can be determined without specific protocols. Reference VO2max and EE were collected on a sample of 32 participants with varying CRF level. CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide evidence that hierarchical modeling is a promising technique for generalized CRF estimation from activities of daily living and personalized EE estimation.
This article is part of the focus theme of Methods of Information in Medicine on "Pervasive ... more This article is part of the focus theme of Methods of Information in Medicine on "Pervasive Intelligent Technologies for Health". Energy Expenditure (EE) estimation algorithms using Heart Rate (HR) or a combination of accelerometer and HR data suffer from large error due to inter-person differences in the relation between HR and EE. We recently introduced a methodology to reduce inter-person differences by predicting a HR normalization parameter during low intensity Activities of Daily Living (ADLs). By using the HR normalization, EE estimation performance was improved, but conditions for performing the normalization automatically in daily life need further analysis. Sedentary lifestyle of many people in western societies urge for an in-depth analysis of the specific ADLs and HR features used to perform HR normalization, and their effects on EE estimation accuracy in participants with varying Physical Activity Levels (PALs). To determine 1) which low intensity ADLs and HR ...
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Papers by Marco Altini
(TM) to discover activity composites from recognized activity
primitives and stay regions in daily living data. Subsequently, we
determine activity composites that are relevant to contextualize
heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activityspecific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including 34 healthy volunteers, we show that HR normalization in 14-day free living data improves accuracy compared to no normalization and normalization based on activity primitives only (29.4% and 19.8% error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by 10.7% in a leave-oneparticipant-out analysis.
determining the causal relation between aspects of human behavior related to physical activity and
health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy
expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation
between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration.
Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and
EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual
calibration or explicit heart rate normalization since CRF accounts for the different relation
between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate
during low intensity activities of daily living, showing that CRF can be determined without specific protocols.
Reference VO2max and EE were collected on a sample of 32 participants with varying CRF level.
CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that
including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation
between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide
evidence that hierarchical modeling is a promising technique for generalized CRF estimation from
activities of daily living and personalized EE estimation.
(TM) to discover activity composites from recognized activity
primitives and stay regions in daily living data. Subsequently, we
determine activity composites that are relevant to contextualize
heart rate (HR). Activity composites were ranked and analyzed to optimize the correlation to HR normalization parameters. Finally, individual-specific HR normalization parameters were used to normalize HR. Normalized HR was then included in activityspecific regression models to estimate EE. Our HR normalization minimizes the effect of individual fitness differences from entering in EE regression models. By estimating HR normalization parameters in free living, our approach avoids dedicated individual calibration or laboratory tests. In a combined free-living and laboratory study dataset, including 34 healthy volunteers, we show that HR normalization in 14-day free living data improves accuracy compared to no normalization and normalization based on activity primitives only (29.4% and 19.8% error reduction against lab reference). Based on acceleration and HR, both recorded from a necklace, and GPS acquired from a smartphone, EE estimation error was reduced by 10.7% in a leave-oneparticipant-out analysis.
determining the causal relation between aspects of human behavior related to physical activity and
health. In this paper we estimate CRF without requiring laboratory protocols and personalize energy
expenditure (EE) estimation models that rely on heart rate data, using CRF. CRF influences the relation
between heart rate and EE. Thus, EE estimation based on heart rate typically requires individual calibration.
Our modeling technique relies on a hierarchical approach using Bayesian modeling for both CRF and
EE estimation models. By including CRF level in a hierarchical Bayesian model, we avoid the need for individual
calibration or explicit heart rate normalization since CRF accounts for the different relation
between heart rate and EE in different individuals. Our method first estimates CRF level from heart rate
during low intensity activities of daily living, showing that CRF can be determined without specific protocols.
Reference VO2max and EE were collected on a sample of 32 participants with varying CRF level.
CRF estimation error could be reduced up to 27.0% compared to other models. Secondly, we show that
including CRF as a group level predictor in a hierarchical model for EE estimation accounts for the relation
between CRF, heart rate and EE. Thus, reducing EE estimation error by 18.2% on average. Our results provide
evidence that hierarchical modeling is a promising technique for generalized CRF estimation from
activities of daily living and personalized EE estimation.