scholarly journals Does the Preferred Walk-Run Transition Speed on Steep Inclines Minimize Energetic Cost, Heart Rate or Neither?

2021 ◽  
pp. jeb.233056
Author(s):  
Jackson W. Brill ◽  
Rodger Kram
Keyword(s):  
Heart Rate ◽  
Linear Regression ◽  
Energetic Cost ◽  
Regression Equations ◽  
Rate Data ◽  
Heart Rate Data ◽  
Transition Speed ◽  
Wide Range ◽  
Incremental Protocol ◽  
Preferred Transition Speed

Humans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0° and 15° trials and on day 2, 5° and 10°. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0°, 5°, and 10°, but the two converged at 15°. Across all inclines, PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.

scholarly journals Does the Preferred Walk-Run Transition Speed on Inclines Minimize Energetic Cost, Heart Rate or Neither?

2020 ◽  
Author(s):  
Jackson W. Brill ◽  
Rodger Kram
Keyword(s):  
Heart Rate ◽  
Linear Regression ◽  
Energetic Cost ◽  
Regression Equations ◽  
Rate Data ◽  
Heart Rate Data ◽  
Transition Speed ◽  
Wide Range ◽  
Incremental Protocol ◽  
Preferred Transition Speed

ABSTRACTHumans prefer to walk at slow speeds and to run at fast speeds. In between, there is a speed at which people choose to transition between gaits, the Preferred Transition Speed (PTS). At slow speeds, it is energetically cheaper to walk and at faster speeds, it is cheaper to run. Thus, there is an intermediate speed, the Energetically Optimal Transition Speed (EOTS). Our goals were to determine: 1) how PTS and EOTS compare across a wide range of inclines and 2) if the EOTS can be predicted by the heart rate optimal transition speed (HROTS). Ten healthy, high-caliber, male trail/mountain runners participated. On day 1, subjects completed 0° and 15° trials and on day 2, 5° and 10°. We calculated PTS as the average of the walk-to-run transition speed (WRTS) and the run-to-walk transition speed (RWTS) determined with an incremental protocol. We calculated EOTS and HROTS from energetic cost and heart rate data for walking and running near the expected EOTS for each incline. The intersection of the walking and running linear regression equations defined EOTS and HROTS. We found that PTS, EOTS, and HROTS all were slower on steeper inclines. PTS was slower than EOTS at 0°, 5°, and 10°, but the two converged at 15°. PTS and EOTS were only moderately correlated. Although EOTS correlated with HROTS, EOTS was not predicted accurately by heart rate on an individual basis.

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 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

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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