scholarly journals Validating accelerometry estimates of energy expenditure across behaviours using heart rate data in a free-living seabird

2017 ◽  
Vol 220 (10) ◽  
pp. 1875-1881 ◽  
Author(s):  
Olivia Hicks ◽  
Sarah Burthe ◽  
Francis Daunt ◽  
Adam Butler ◽  
Charles Bishop ◽  
...  
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Rate Data ◽  
Free Living ◽  
Heart Rate Data

scholarly journals 785: Early labour detection in laboratory and free-living conditions using combined electrohysterography and heart rate data

2019 ◽  
Vol 220 (1) ◽  
pp. S513-S514
Author(s):  
Marco Altini ◽  
Michiel Rooijakkers ◽  
Elisa Rossetti ◽  
Julien Penders ◽  
Pauline Dreesen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Living Conditions ◽  
Rate Data ◽  
Free Living ◽  
Heart Rate Data ◽  
Early Labour ◽  
Free Living Conditions

scholarly journals Improving Energy Expenditure Estimation in Wrist-Worn Wearables by Augmenting Heart Rate Data With Heat Flux Measurement

2021 ◽  
Vol 70 ◽  
pp. 1-8
Author(s):  
Saku Levikari ◽  
Antti Immonen ◽  
Mikko Kuisma ◽  
Heikki Peltonen ◽  
Mika Silvennoinen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heat Flux ◽  
Energy Expenditure ◽  
Flux Measurement ◽  
Rate Data ◽  
Heart Rate Data ◽  
Heat Flux Measurement

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Estimation of Dog Emotions by Machine Learning Using Heart Rate Data of Other Dogs

2021 ◽  
Vol 2021 (0) ◽  
pp. 2P1-M03
Author(s):  
Kotaro SATO ◽  
Kazunori OHNO ◽  
Ryoichiro TAMURA ◽  
Sandeep Kumar NAYAK ◽  
Shotaro KOJIMA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Rate Data ◽  
Heart Rate Data

scholarly journals The Impact of Accelerometer and Heart Rate Data on Hypoglycemia Mitigation in Type 1 Diabetes

2014 ◽  
Vol 8 (1) ◽  
pp. 64-69 ◽  
Author(s):  
Matthew Stenerson ◽  
Fraser Cameron ◽  
Darrell M. Wilson ◽  
Breanne Harris ◽  
Shelby Payne ◽  
...  
Keyword(s):  
Heart Rate ◽  
Type 1 Diabetes ◽  
Rate Data ◽  
Heart Rate Data ◽  
The Impact

scholarly journals Review of Prediction Models to Estimate Activity-Related Energy Expenditure in Children and Adolescents

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Suzanne M. de Graauw ◽  
Janke F. de Groot ◽  
Marco van Brussel ◽  
Marjolein F. Streur ◽  
Tim Takken
Keyword(s):  
Heart Rate ◽  
Energy Expenditure ◽  
Prediction Model ◽  
Children And Adolescents ◽  
Prediction Models ◽  
Accurate Estimate ◽  
Healthy Children ◽  
Triaxial Accelerometer ◽  
Free Living ◽  
Doubly Labelled Water

Purpose. To critically review the validity of accelerometry-based prediction models to estimate activity energy expenditure (AEE) in children and adolescents.Methods. The CINAHL, EMBASE, PsycINFO, and PubMed/MEDLINE databases were searched. Inclusion criteria were development or validation of an accelerometer-based prediction model for the estimation of AEE in healthy children or adolescents (6–18 years), criterion measure: indirect calorimetry, or doubly labelled water, and language: Dutch, English or German.Results. Nine studies were included. Median methodological quality was5.5±2.0 IR (out of a maximum 10 points). Prediction models combining heart rate and counts explained 86–91% of the variance in measured AEE. A prediction model based on a triaxial accelerometer explained 90%. Models derived during free-living explained up to 45%.Conclusions. Accelerometry-based prediction models may provide an accurate estimate of AEE in children on a group level. Best results are retrieved when the model combines accelerometer counts with heart rate or when a triaxial accelerometer is used. Future development of AEE prediction models applicable to free-living scenarios is needed.

Prediction of Menstrual Cycle Phase by Wearable Heart Rate Sensor

2021 ◽  
pp. 1-15
Author(s):  
Junichiro Hayano ◽  
Emi Yuda
Keyword(s):  
Heart Rate ◽  
Menstrual Cycle ◽  
Technological Development ◽  
Unmet Need ◽  
Cycle Phase ◽  
Rate Data ◽  
Menstrual Cycle Phase ◽  
Heart Rate Data ◽  
Rate Sensor

The prediction of the menstrual cycle phase and fertility window by easily measurable bio-signals is an unmet need and such technological development will greatly contribute to women's QoL. Although many studies have reported differences in autonomic indices of heart rate variability (HRV) between follicular and luteal phases, they have not yet reached the level that can predict the menstrual cycle phases. The recent development of wearable sensors-enabled heart rate monitoring during daily life. The long-term heart rate data obtained by them carry plenty of information, and the information that can be extracted by conventional HRV analysis is only a limited part of it. This chapter introduces comprehensive analyses of long-term heart rate data that may be useful for revealing their associations with the menstrual cycle phase.

Self-Reported Wearable Heart Rate Data May Be Useful in the Diagnosis and Treatment of Hyperthyroidism

2020 ◽  
Vol 32 (5) ◽  
pp. 242-244 ◽  
Author(s):  
Aaron B. Neinstein ◽  
Michael Blum ◽  
Umesh Masharani
Keyword(s):  
Heart Rate ◽  
Diagnosis And Treatment ◽  
Rate Data ◽  
Heart Rate Data

scholarly journals Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device

SLEEP ◽  
2019 ◽  
Vol 42 (12) ◽  
Author(s):  
Olivia Walch ◽  
Yitong Huang ◽  
Daniel Forger ◽  
Cathy Goldstein
Keyword(s):  
Heart Rate ◽  
Sleep Stage ◽  
Nrem Sleep ◽  
Neural Nets ◽  
Wearable Device ◽  
Sensor Data ◽  
Rate Data ◽  
Mathematical Methods ◽  
Heart Rate Data ◽  
Using Data

Abstract Wearable, multisensor, consumer devices that estimate sleep are now commonplace, but the algorithms used by these devices to score sleep are not open source, and the raw sensor data is rarely accessible for external use. As a result, these devices are limited in their usefulness for clinical and research applications, despite holding much promise. We used a mobile application of our own creation to collect raw acceleration data and heart rate from the Apple Watch worn by participants undergoing polysomnography, as well as during the ambulatory period preceding in lab testing. Using this data, we compared the contributions of multiple features (motion, local standard deviation in heart rate, and “clock proxy”) to performance across several classifiers. Best performance was achieved using neural nets, though the differences across classifiers were generally small. For sleep-wake classification, our method scored 90% of epochs correctly, with 59.6% of true wake epochs (specificity) and 93% of true sleep epochs (sensitivity) scored correctly. Accuracy for differentiating wake, NREM sleep, and REM sleep was approximately 72% when all features were used. We generalized our results by testing the models trained on Apple Watch data using data from the Multi-ethnic Study of Atherosclerosis (MESA), and found that we were able to predict sleep with performance comparable to testing on our own dataset. This study demonstrates, for the first time, the ability to analyze raw acceleration and heart rate data from a ubiquitous wearable device with accepted, disclosed mathematical methods to improve accuracy of sleep and sleep stage prediction.

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