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Bloom Technologies

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Patent
Bloom Technologies | Date: 2017-02-10

Systems and methods for monitoring the onset or occurrence of labor contractions and detecting or estimating labor in a pregnant female are provided.


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.


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.


PubMed | HIGH-TECH, Bloom Technologies and University of Passau
Type: | Journal: Artificial intelligence in medicine | Year: 2016

In this paper we propose artificial intelligence methods to estimate cardiorespiratory fitness (CRF) in free-living using wearable sensor data.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 participants habitual behavior in free-living.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.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.


PubMed | HIGH-TECH, Bloom Technologies and University of Passau
Type: | Journal: 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.


Trademark
Bloom Technologies | Date: 2014-12-18

Wearable sensors to detect the electrical and physiological signals of the body.


Trademark
Bloom Technologies | Date: 2016-07-14

Wearable sensors to detect the electrical and physiological signals of the body not for medical use. Wearable sensors to detect the electrical and physiological signals of the body for medical use.


A system for detecting and quantifying deviations from physiological signals normality and methods for making and using same. Each subject physiology follows unique patterns. The physiological signals can be affected by one or more factors such as circadian rhythm, disease and/or external stressors. Deviations of physiological signals from the normality of a subject can be indicative of external events that might require proper lifestyle management or just in time interventions, such as being exposed to high stress or the progress/onset of specific disease conditions. The disclosed system advantageously can quantify such deviations.


Patent
Bloom Technologies | Date: 2014-10-27

A system suitable for generating messages based on biometric and contextual data and methods for making and using the same. One or more sensors can be used to measure biometric and/or contextual data. The measured data are analyzed using behavior analytics to capture behavior of a selected user. The behavior is analyzed with messaging analytics to generate personalized messages related to the behavior. The message preferably is personalized at at least one level, such as time, place and format of message delivery, content of the message, and tone of the message. The message personalization can further adapt to changes in the users behavior and/or preferences. Thereby, the method and system advantageously can provide biometric and context based messaging for motivating healthy behavior.


Grant
Agency: European Commission | Branch: H2020 | Program: SME-1 | Phase: SMEInst-01-2016-2017 | Award Amount: 71.43K | Year: 2016

Preterm birth (PTB) is the leading cause of neonatal death and the second-leading cause of death in children under five years. In Europe, about 75% of all neonatal deaths occur to infants born preterm. In addition, children who are born prematurely have higher rates of cerebral palsy, sensory deficits, learning disability and respiratory illnesses. Spontaneous preterm labour is the cause of almost 50% of cases of PTB. PTB global rates vary from 5% to 18% of births. PTB causes a great suffering to new parents and has also a significant economic impact. In 2007, the Institute of Medicine reported that the medical cost associated with premature birth in the US was $16,9 billion per year and when considering indirect costs, this figure rises to $ 26,2 billion in total. Approximately 10% of pregnant woman have high-risk factors of PTB. An early detection and intervention is crucial as there are several medical options to delay labour, and protect the baby. However, at present the only way of detecting PTB is to go to hospital for clinical examination. Expecting couples may go several times to the hospital with suspicious of preterm labour, and be sent home a couple of hours later. These false alarms directly lead to excessive use of healthcare and add to the overall cost burden. Bloom technologies will cover this gap and reduce these impacts offering a disruptive solution: WISH, a wearable patch, based in contractions measurement, for pregnancy monitoring focused on early labour detection. WISH will allow the early detection of PTB to decrease its health and economic impact. WISH is based in measurement of uterine electric activity to detect contractions. Through additional measurement of other physiological parameters we will offer an effective way of self-control to our main target user: women with high risk of PTB. Our business opportunity relies in the 12,6 million high risk mothers in the EU and US, that will be potential users of WISH.

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