scholarly journals Measuring Vascular Recovery Rate After Exercise

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 12 ◽  
Author(s):  
Halil Dijab ◽  
Jordi Alastruey ◽  
Peter Charlton
Keyword(s):  
Heart Rate ◽  
Recovery Rate ◽  
Wearable Sensors ◽  
Cardiovascular Fitness ◽  
Wave Shape ◽  
Future Studies ◽  
Additional Information ◽  
Novel Approach ◽  
Pulse Waves ◽  
Future Work

The rate at which an individual recovers from exercise is known to be indicative of cardiovascular risk. It has been widely shown that the reduction in heart rate immediately after exercise is predictive of mortality. However, little research has been conducted into whether the time taken for the blood vessels to return to normal is also indicative of risk. In this study, we present a novel approach to assess vascular recovery rate (VRR) using the photoplethysmogram (PPG) signal, which is monitored by smart wearables. The VORTAL dataset (http://peterhcharlton.github.io/RRest/) was used for this study, containing PPG signals from 39 healthy subjects before (baseline) and after exercise. 31 VRR indices were extracted from the PPG pulse wave shape, as well as heart rate for comparison. The rate at which indices returned to baseline after exercise was quantified, and the consistency of changes between subjects was assessed statistically. Many VRR indices exhibited changes after exercise which were consistent between subjects. Indices derived from the timings and second derivative of pulse waves were identified as candidates for future work. The rate at which the indices returned to baseline differed between indices and subjects, indicating that they may provide additional information beyond that of heart rate, and that they may be useful for stratifying subjects. This study demonstrated the feasibility of assessing VRR after exercise from the PPG. Future studies should investigate whether VRR indices are associated with cardiovascular fitness, and the potential utility of incorporating the indices into wearable sensors.

scholarly journals Alzheimer’s Disease: A Step Towards Prognosis Using Smart Wearables

Proceedings ◽  
2018 ◽  
Vol 4 (1) ◽  
pp. 8 ◽  
Author(s):  
Antonella D. Pontoriero ◽  
Peter H. Charlton ◽  
Jordi Alastruey
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Arterial Stiffness ◽  
Wearable Sensors ◽  
Lifestyle Changes ◽  
High Systolic Blood Pressure ◽  
Novel Approach ◽  
Patients At Risk ◽  
Pulse Waves

Alzheimer’s disease (AD) is the most common cause of dementia. Several haemodynamic risk factors for AD have been identified, including ageing, increased arterial stiffness, high systolic blood pressure (BP) and brain hypoperfusion. We propose a novel approach for assessing haemodynamic risk factors by analysing arterial pulse waves (PWs). The aim of this feasibility study was to determine whether features extracted from PWs measured by wearable sensors might have utility for stratifying patients at risk of AD. A numerical model of PW propagation was used to simulate PWs for virtual subjects of each age decade from 25 to 75 years (16 subjects in total), with subjects at each age exhibiting normal variation in arterial stiffness. Several PW features were extracted, and their relationships with AD risk factors were investigated. PWs at the wrist were found to exhibit changes with age and arterial stiffness, indicating that it may be possible to identify changes in risk factors from smart wearables. Several candidate PW features were identified which changed significantly with age for future testing. This study demonstrates the potential feasibility of assessing haemodynamic risk factors for AD from non-invasive PWs. These factors could be assessed from the PPG PW, which can be acquired by smart watches and phones. If the findings are replicated in clinical studies, then this may provide opportunities for patients to assess their own risk and make lifestyle changes accordingly.

scholarly journals VITAL SIGNS MONITORING BASED ON WEBCAM FOR HOME TELEMEDICINE APPLICATIONS

2021 ◽  
Author(s):  
GUOMING CHEN GUOMING CHEN ◽  
QI ZHANG ◽  
XUANKE TONG ◽  
GUOFU LIAO
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Video Processing ◽  
Wearable Sensors ◽  
Vital Signs ◽  
Low Frequency ◽  
Experimental System ◽  
The Real ◽  
Vital Signs Monitoring ◽  
Novel Approach

Abstract. In this paper, we proposed a novel approach to monitor the vital signs based webcam for home telemedicine applications. This approach can continuously monitor the vital signs without wearable sensors. It uses the real time video processing algorithm to obtain the instantaneous heart rate(HR) and respiration rate(RR). Furthermore, the heart rate variability (HRV) was analyzed by power spectral density (PSD) estimation using the Lomb periodogram. Experiments in different scenarios were performed to verify the efficacy of the proposed noncontact monitoring vital signs based on webcam. The real time experimental system can be used to measure the instantaneous HR and RR, at the same time the low frequency and high frequency components were extracted. All experimental results show that the proposed concept can be applied to the home telemedicine in the future.

Heart Rate Variability, Homeostasis, and Brain Function

2012 ◽  
Vol 26 (4) ◽  
pp. 178-203 ◽  
Author(s):  
Francesco Riganello ◽  
Sergio Garbarino ◽  
Walter G. Sannita
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Communication Disorders ◽  
Affective States ◽  
Experimental Conditions ◽  
Cardiovascular Research ◽  
Sensory Stimuli ◽  
Additional Information ◽  
Brain Injured

Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

scholarly journals Wearable Technologies in Field Hockey Competitions: A Scoping Review

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5242
Author(s):  
Jolene Ziyuan Lim ◽  
Alexiaa Sim ◽  
Pui Wah Kong
Keyword(s):  
Heart Rate ◽  
Inertial Sensors ◽  
Wearable Devices ◽  
Field Hockey ◽  
Heart Rate Monitors ◽  
Search Terms ◽  
Physiological Demands ◽  
And Performance ◽  
Future Work ◽  
Skin Temperatures

The aim of this review is to investigate the common wearable devices currently used in field hockey competitions, and to understand the hockey-specific parameters these devices measure. A systematic search was conducted by using three electronic databases and search terms that included field hockey, wearables, accelerometers, inertial sensors, global positioning system (GPS), heart rate monitors, load, performance analysis, player activity profiles, and competitions from the earliest record. The review included 39 studies that used wearable devices during competitions. GPS units were found to be the most common wearable in elite field hockey competitions, followed by heart rate monitors. Wearables in field hockey are mostly used to measure player activity profiles and physiological demands. Inconsistencies in sampling rates and performance bands make comparisons between studies challenging. Nonetheless, this review demonstrated that wearable devices are being used for various applications in field hockey. Researchers, engineers, coaches, and sport scientists can consider using GPS units of higher sampling rates, as well as including additional variables such as skin temperatures and injury associations, to provide a more thorough evaluation of players’ physical and physiological performances. Future work should include goalkeepers and non-elite players who are less studied in the current literature.

scholarly journals Design and evaluation of a novel approach to invisible electrocardiography (ECG) in sanitary facilities using polymeric electrodes

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aline dos Santos Silva ◽  
Hugo Almeida ◽  
Hugo Plácido da Silva ◽  
António Oliveira
Keyword(s):  
Heart Rate ◽  
Pearson Correlation ◽  
Voluntary Action ◽  
Signal Loss ◽  
Standard Equipment ◽  
Self Monitoring ◽  
Battery Lifetime ◽  
Ecg Signals ◽  
Toilet Seat ◽  
Novel Approach

AbstractMultiple wearable devices for cardiovascular self-monitoring have been proposed over the years, with growing evidence showing their effectiveness in the detection of pathologies that would otherwise be unnoticed through standard routine exams. In particular, Electrocardiography (ECG) has been an important tool for such purpose. However, wearables have known limitations, chief among which are the need for a voluntary action so that the ECG trace can be taken, battery lifetime, and abandonment. To effectively address these, novel solutions are needed, which has recently paved the way for “invisible” (aka “off-the-person”) sensing approaches. In this article we describe the design and experimental evaluation of a system for invisible ECG monitoring at home. For this purpose, a new sensor design was proposed, novel materials have been explored, and a proof-of-concept data collection system was created in the form of a toilet seat, enabling ECG measurements as an extension of the regular use of sanitary facilities, without requiring body-worn devices. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard equipment, involving 10 healthy subjects. For the acquisition of the ECG signals on the toilet seat, polymeric electrodes with different textures were produced and tested. According to the results obtained, some of the textures did not allow the acquisition of signals in all users. However, a pyramidal texture showed the best results in relation to heart rate and ECG waveform morphology. For a texture that has shown 0% signal loss, the mean heart rate difference between the reference and experimental device was − 1.778 ± 4.654 Beats per minute (BPM); in terms of ECG waveform, the best cases present a Pearson correlation coefficient above 0.99.

scholarly journals 651 Longitudinal, unobtrusive, and ecologically valid sleep metric estimation from a smart bed to predict the pathology of COVID-19

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A255-A255
Author(s):  
Dmytro Guzenko ◽  
Gary Garcia ◽  
Farzad Siyahjani ◽  
Kevin Monette ◽  
Susan DeFranco ◽  
...  
Keyword(s):  
Heart Rate ◽  
Sleep Quality ◽  
Predictive Model ◽  
Sleep Duration ◽  
Wearable Sensors ◽  
Test Results ◽  
Cloud Data ◽  
Cardiorespiratory Function ◽  
Group Data ◽  
Rich Information

Abstract Introduction Pathophysiologic responses to viral respiratory challenges such as SARS-CoV-2 may affect sleep duration, quality and concomitant cardiorespiratory function. Unobtrusive and ecologically valid methods to monitor longitudinal sleep metrics may therefore have practical value for surveillance and monitoring of infectious illnesses. We leveraged sleep metrics from Sleep Number 360 smart bed users to build a COVID-19 predictive model. Methods An IRB approved survey was presented to opting-in users from August to November 2020. COVID-19 test results were reported by 2003/6878 respondents (116 positive; 1887 negative). From the positive group, data from 82 responders (44.7±11.3 yrs.) who reported the date of symptom onset were used. From the negative group, data from 1519 responders (48.4±12.9 yrs.) who reported testing dates were used. Sleep duration, sleep quality, restful sleep duration, time to fall asleep, respiration rate, heart rate, and motion level were obtained from ballistocardiography signals stored in the cloud. Data from January to October 2020 were considered. The predictive model consists of two levels: 1) the daily probability of staying healthy calculated by logistic regression and 2) a continuous density Hidden Markov Model to refine the daily prediction considering the past decision history. Results With respect to their baseline, significant increases in sleep duration, average breathing rate, average heart rate and decrease in sleep quality were associated with symptom exacerbation in COVID-19 positive respondents. In COVID-19 negative respondents, no significant sleep or cardiorespiratory metrics were observed. Evaluation of the predictive model resulted in cross-validated area under the receiving-operator curve (AUC) estimate of 0.84±0.09 which is similar to values reported for wearable-sensors. Considering additional days to confirm prediction improved the AUC estimate to 0.93±0.05. Conclusion The results obtained on the smart bed user population suggest that unobtrusive sleep metrics may offer rich information to predict and track the development of symptoms in individuals infected with COVID-19. Support (if any):

scholarly journals Functional prediction of environmental variables using metabolic networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Adèle Weber Zendrera ◽  
Nataliya Sokolovska ◽  
Hédi A. Soula
Keyword(s):  
Machine Learning ◽  
Growth Temperature ◽  
Environmental Variables ◽  
Metabolic Networks ◽  
Machine Learning Techniques ◽  
Underlying Structure ◽  
Glutathione Biosynthesis ◽  
Additional Information ◽  
Cold Environments ◽  
Novel Approach

AbstractIn this manuscript, we propose a novel approach to assess relationships between environment and metabolic networks. We used a comprehensive dataset of more than 5000 prokaryotic species from which we derived the metabolic networks. We compute the scope from the reconstructed graphs, which is the set of all metabolites and reactions that can potentially be synthesized when provided with external metabolites. We show using machine learning techniques that the scope is an excellent predictor of taxonomic and environmental variables, namely growth temperature, oxygen tolerance, and habitat. In the literature, metabolites and pathways are rarely used to discriminate species. We make use of the scope underlying structure—metabolites and pathways—to construct the predictive models, giving additional information on the important metabolic pathways needed to discriminate the species, which is often absent in other metabolic network properties. For example, in the particular case of growth temperature, glutathione biosynthesis pathways are specific to species growing in cold environments, whereas tungsten metabolism is specific to species in warm environments, as was hinted in current literature. From a machine learning perspective, the scope is able to reduce the dimension of our data, and can thus be considered as an interpretable graph embedding.

Wearable Sensors and Their Metrics for Measuring Comprehensive Occupational Fatigue: A Scoping Review

2017 ◽  
Vol 61 (1) ◽  
pp. 1041-1045 ◽  
Author(s):  
Yibo Zhu ◽  
Rasik R Jankay ◽  
Laura C Pieratt ◽  
Ranjana K. Mehta
Keyword(s):  
Heart Rate ◽  
Scoping Review ◽  
Wearable Sensors ◽  
Health And Safety ◽  
Exclusion Criteria ◽  
Multidimensional Construct ◽  
Ovid Medline ◽  
Rate Sensor ◽  
Sleep Length ◽  
Occupational Fatigue

Extensive research has been conducted to study the effects of physical and sleep related fatigue on occupational health and safety. However, fatigue is a complex multidimensional construct, that is task- and occupation-dependent, and our knowledge on how to measure this complex construct is limited. A scoping review was conducted to: 1) review sensors and their metrics currently employed in occupational fatigue studies, 2) identify overlap between sensors and associated metrics that can be leveraged to assess comprehensive fatigue, 3) investigating the effectiveness of the sensors/metrics, and 4) recommended potential sensor/metric combinations to evaluate comprehensive fatigue. 512 unique abstracts were identified through Ovid-MEDLINE, MEDLINE, Embase and Cinal databases and application of the inclusion/exclusion criteria resulted in 27 articles that were included for the review. Heart rate sensors and actigraphs were identified to be the most suitable devices to study comprehensive fatigue. Heart rate trend within the heart rate sensor, and sleep length and sleep efficiency within actigraphs were found to be the most popular and reliable metrics for measuring occupational fatigue.

scholarly journals Lactate, Heart Rate and Rating of Perceived Exertion Responses to Shorter and Longer Duration CrossFit® Training Sessions

2018 ◽  
Vol 3 (4) ◽  
pp. 60 ◽  
Author(s):  
Ramires Tibana ◽  
Nuno de Sousa ◽  
Jonato Prestes ◽  
Fabrício Voltarelli
Keyword(s):  
Heart Rate ◽  
Perceived Exertion ◽  
Recovery Period ◽  
Lactate Concentration ◽  
Blood Lactate Concentration ◽  
Cardiovascular Responses ◽  
Cardiovascular Fitness ◽  
Rating Of Perceived Exertion ◽  
Training Experience ◽  
Significant Difference

The aim of this study was to analyze blood lactate concentration (LAC), heart rate (HR), and rating perceived exertion (RPE) during and after shorter and longer duration CrossFit® sessions. Nine men (27.7 ± 3.2 years; 11.3 ± 4.6% body fat percentage and training experience: 41.1 ± 19.6 months) randomly performed two CrossFit® sessions (shorter: ~4 min and longer: 17 min) with a 7-day interval between them. The response of LAC and HR were measured pre, during, immediately after, and 10, 20, and 30 min after the sessions. RPE was measured pre and immediately after sessions. Lactate levels were higher during the recovery of the shorter session as compared with the longer session (shorter: 15.9 ± 2.2 mmol/L/min, longer: 12.6 ± 2.6 mmol/L/min; p = 0.019). There were no significant differences between protocols on HR during (shorter: 176 ± 6 bpm or 91 ± 4% HRmax, longer: 174 ± 3 bpm or 90 ± 3% HRmax, p = 0.387). The LAC was significantly higher throughout the recovery period for both training sessions as compared to pre-exercise. The RPE was increased immediately after both sessions as compared to pre-exercise, while there was no significant difference between them (shorter: 8.7 ± 0.9, longer: 9.6 ± 0.5; p = 0.360). These results demonstrated that both shorter and longer sessions induced elevated cardiovascular responses which met the recommendations for gains in cardiovascular fitness. In addition, both training sessions had a high metabolic and perceptual response, which may not be suitable if performed on consecutive days.

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