Entity

Time filter

Source Type

San Francisco, CA, United States

Altini M.,Bloom Technologies | Altini M.,TU Eindhoven | Casale P.,IMEC | Penders J.F.,IMEC | And 2 more authors.
IEEE Journal of Biomedical and Health Informatics | Year: 2015

We introduce an approach to personalize energy expenditure (EE) estimates in free living. First, we use topic models 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 activity-specific 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-one-participant-out analysis. © 2013 IEEE. Source


Altini M.,Bloom Technologies | Casale P.,HIGH-TECH | Penders J.,HIGH-TECH | Amft O.,University of Passau
Artificial Intelligence in Medicine | Year: 2016

Objective: In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data. Methods: Our methods rely on a computational framework able to contextualize heart rate (HR) in free-living, and use context-specific HR as predictor of CRF without need for laboratory tests. In particular, we propose three estimation steps. Initially, we recognize activity primitives using accelerometer and location data. Using topic models, we group activity primitives and derive activities composites. We subsequently rank activity composites, and analyze the relation between ranked activity composites and CRF across individuals. Finally, HR data in specific activity primitives and composites is used as predictor in a hierarchical Bayesian regression model to estimate CRF level from the participant's habitual behavior in free-living. Results: We show that by combining activity primitives and activity composites the proposed framework can adapt to the user and context, and outperforms other CRF estimation models, reducing estimation error between 10.3% and 22.6% on a study population of 46 participants. Conclusions: Our investigation showed that HR can be contextualized in free-living using activity primitives and activity composites and robust CRF estimation in free-living is feasible. © 2016 Elsevier B.V. Source


Altini M.,Bloom Technologies | Altini M.,TU Eindhoven | Penders J.,Holst Center | Amft O.,University of Passau | Amft O.,TU Eindhoven
IEEE Journal of Biomedical and Health Informatics | Year: 2016

In this paper, we present a method to estimate oxygen uptake (V O2) during daily life activities and transitions between them. First, we automatically locate transitions between activities and periods of nonsteady-state V O2. Subsequently, we propose and compare activity-specific linear functions to model steady-state activities and transition-specific nonlinear functions to model nonsteady-state activities and transitions. We evaluate our approach in study data from 22 participants that wore a combined accelerometer and heart rate sensor while performing a wide range of activities (clustered into lying, sedentary, dynamic/household, walking, biking and running), including many transitions between intensities, thus resulting in nonsteady-state V O2. Indirect calorimetry was used in parallel to obtain V O2 reference. V O2 estimation error during transitions between sedentary, household and walking activities could be reduced by 16% on average using the proposed approach, compared to state of the art methods. © 2015 IEEE. Source


Altini M.,TU Eindhoven | Altini M.,Bloom Technologies | Casale P.,HIGH-TECH | Penders J.,HIGH-TECH | Amft O.,University of Passau
Journal of Biomedical Informatics | Year: 2015

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. © 2015 Elsevier Inc. Source


Casale P.,Holst Center | Casale P.,TU Eindhoven | Altini M.,TU Eindhoven | Altini M.,Bloom Technologies | Amft O.,University of Passau
IEEE Internet of Things Journal | Year: 2015

We investigate the process of transferring the activity recognition models within the nodes of a body sensor network (BSN). In particular, we propose a methodology that supports and makes the transferring possible. Based on a collaborative training strategy, classifier ensembles of randomized trees are used to create activity recognition models that can successfully be transferred within the nodes of the network. The methodology has been applied in scenarios where a node present in the network is replaced by a new node located in the same position (replacement scenario) and relocated to a previously unknown position (relocation scenario). Experimental results show that the transferred recognition models achieve high-recognition performance in the replacement scenario and good-recognition performance are achieved in the relocation scenario. Results have been validated with multiple K-folds cross-validations in order to test the performance of the methodology when different amount of data are shared between nodes. © 2015 IEEE. Source

Discover hidden collaborations