scholarly journals A Video-Based Technique for Heart Rate and Eye Blinks Rate Estimation: A Potential Solution for Telemonitoring and Remote Healthcare

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1607
Author(s):  
Vincenzo Ronca ◽  
Andrea Giorgi ◽  
Dario Rossi ◽  
Antonello Di Florio ◽  
Gianluca Di Flumeri ◽  
...  
Keyword(s):  
Heart Rate ◽  
Resting State ◽  
Video Conferencing ◽  
Wearable Sensors ◽  
Objective Evaluation ◽  
Healthcare Applications ◽  
Laboratory Equipment ◽  
Eye Blinks ◽  
Remote Healthcare ◽  
Nominal Condition

Current telemedicine and remote healthcare applications foresee different interactions between the doctor and the patient relying on the use of commercial and medical wearable sensors and internet-based video conferencing platforms. Nevertheless, the existing applications necessarily require a contact between the patient and sensors for an objective evaluation of the patient’s state. The proposed study explored an innovative video-based solution for monitoring neurophysiological parameters of potential patients and assessing their mental state. In particular, we investigated the possibility to estimate the heart rate (HR) and eye blinks rate (EBR) of participants while performing laboratory tasks by mean of facial—video analysis. The objectives of the study were focused on: (i) assessing the effectiveness of the proposed technique in estimating the HR and EBR by comparing them with laboratory sensor-based measures and (ii) assessing the capability of the video—based technique in discriminating between the participant’s resting state (Nominal condition) and their active state (Non-nominal condition). The results demonstrated that the HR and EBR estimated through the facial—video technique or the laboratory equipment did not statistically differ (p > 0.1), and that these neurophysiological parameters allowed to discriminate between the Nominal and Non-nominal states (p < 0.02).

scholarly journals Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4448 ◽  
Author(s):  
Günther Sagl ◽  
Bernd Resch ◽  
Andreas Petutschnig ◽  
Kalliopi Kyriakou ◽  
Michael Liedlgruber ◽  
...  
Keyword(s):  
Heart Rate ◽  
Galvanic Skin Response ◽  
Low Cost ◽  
Wearable Sensors ◽  
Sensor Data ◽  
Quantified Self ◽  
High Quality ◽  
Laboratory Equipment ◽  
Skin Response ◽  
Physiological Sensors

Wearable sensors are increasingly used in research, as well as for personal and private purposes. A variety of scientific studies are based on physiological measurements from such rather low-cost wearables. That said, how accurate are such measurements compared to measurements from well-calibrated, high-quality laboratory equipment used in psychological and medical research? The answer to this question, undoubtedly impacts the reliability of a study’s results. In this paper, we demonstrate an approach to quantify the accuracy of low-cost wearables in comparison to high-quality laboratory sensors. We therefore developed a benchmark framework for physiological sensors that covers the entire workflow from sensor data acquisition to the computation and interpretation of diverse correlation and similarity metrics. We evaluated this framework based on a study with 18 participants. Each participant was equipped with one high-quality laboratory sensor and two wearables. These three sensors simultaneously measured the physiological parameters such as heart rate and galvanic skin response, while the participant was cycling on an ergometer following a predefined routine. The results of our benchmarking show that cardiovascular parameters (heart rate, inter-beat interval, heart rate variability) yield very high correlations and similarities. Measurement of galvanic skin response, which is a more delicate undertaking, resulted in lower, but still reasonable correlations and similarities. We conclude that the benchmarked wearables provide physiological measurements such as heart rate and inter-beat interval with an accuracy close to that of the professional high-end sensor, but the accuracy varies more for other parameters, such as galvanic skin response.

scholarly journals Adaptability Analysis of 6LoWPAN and RPL for Healthcare applications of Internet-of-Things

2021 ◽  
Vol 2 (2) ◽  
pp. 69-81
Author(s):  
Jayaram Hariharakrishnan ◽  
Bhalaji N
Keyword(s):  
Internet Of Things ◽  
Wearable Sensors ◽  
Geographical Area ◽  
Training Session ◽  
Medical Professionals ◽  
Human Beings ◽  
Healthcare Applications ◽  
Unexpected Death ◽  
Personal Training ◽  
Remote Healthcare

Ubiquitous Networks powered by Wireless Sensor Networks (WSN) is cutting across many technologies assisting day-to-day human activities. This technology confers the ability to sense and surmise the external environmental factors of various ecologies. Interconnection of these sensing devices for Machine to Machine (M2M) communication leads to the origination of Internet-of-Things (IoT). Recent advancements in the technology of Internet-of-Things guides the production of smart objects that can accomplish location, identification, connection and measurement of external factors. This leads to a new type of communication paradigm between objects and humans. One of the important problem due to the population explosion that can be addressed by IoT is the Healthcare of individual human beings. Remote health monitoring is one of the greatest technology exploited in medical professionals to keep a check on the patient’s important health factors periodically. This was done in a smaller geographical area before the era of IoT. As IoT can communicate to other Internet, This remote healthcare monitoring can now be applied over a wider geographical topology. This paper extensively analyses the performance of 6LoWPAN and RPL IoT for healthcare applications. This paper especially focuses on monitoring an athlete's thermoregulation. Also, a new technique to identify and train marathon athletes to the race topography has been proposed. In this technique, each athlete is fitted with wearable sensors in their body in the training session to monitor and analyze the thermoregulation process occurring during training. After a detailed analysis of the athletes’ thermoregulation process, personal training schedules are charted down according to variation in the thermoregulation process in each athlete. This technique helps to monitor each athlete’s progress personally with less number of coaches and medical professionals leading to the prevention of unexpected death of a healthy athlete.

Exploring the Applications of Machine Learning in Healthcare

2020 ◽  
Vol 10 (4) ◽  
pp. 458-472
Author(s):  
Tausifa Jan Saleem ◽  
Mohammad Ahsan Chishti
Keyword(s):  
Machine Learning ◽  
Disease Risk ◽  
Disease Diagnosis ◽  
Machine Intelligence ◽  
Healthcare Applications ◽  
Comprehensive Overview ◽  
Machine Learning Applications ◽  
Remote Healthcare ◽  
Healthcare Monitoring ◽  
Applications Of Machine Learning

The rapid progress in domains like machine learning, and big data has created plenty of opportunities in data-driven applications particularly healthcare. Incorporating machine intelligence in healthcare can result in breakthroughs like precise disease diagnosis, novel methods of treatment, remote healthcare monitoring, drug discovery, and curtailment in healthcare costs. The implementation of machine intelligence algorithms on the massive healthcare datasets is computationally expensive. However, consequential progress in computational power during recent years has facilitated the deployment of machine intelligence algorithms in healthcare applications. Motivated to explore these applications, this paper presents a review of research works dedicated to the implementation of machine learning on healthcare datasets. The studies that were conducted have been categorized into following groups (a) disease diagnosis and detection, (b) disease risk prediction, (c) health monitoring, (d) healthcare related discoveries, and (e) epidemic outbreak prediction. The objective of the research is to help the researchers in this field to get a comprehensive overview of the machine learning applications in healthcare. Apart from revealing the potential of machine learning in healthcare, this paper will serve as a motivation to foster advanced research in the domain of machine intelligence-driven healthcare.

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):

RELATIONSHIP BETWEEN SURGEON'S PHYSIOLOGICAL AND KINEMATIC PARAMETERS AND THE QUALITY OF SURGICAL PERFORMANCE AND WORKLOAD IN LAPAROSCOPIC TRAINING

2021 ◽  
Vol 108 (Supplement_3) ◽  
Author(s):  
J A Sánchez-Margallo ◽  
J Castillo Rabazo ◽  
D Durán Rey ◽  
I López-Agudelo ◽  
M R González-Portillo ◽  
...  
Keyword(s):  
Heart Rate ◽  
Objective Evaluation ◽  
Laparoscopic Training ◽  
Evaluation Tool ◽  
Surgical Performance ◽  
Hand Motion ◽  
Task Load ◽  
Tissue Manipulation ◽  
Level Of Experience

Abstract INTRODUCTION This study presents the first steps and results towards the development of a system for predicting the quality of surgical performance and workload in laparoscopic training. MATERIAL AND METHODS Surgeons wore a smartwatch which recorded their heart rate and hand motion during each laparoscopic procedure. Data was then correlated with both the Surgery Task Load Index (SURG-TLX) subjective questionnaire and the Global Operational Assessment of Laparoscopic Skills (GOALS) objective evaluation tool. Statistical analysis was conducted in order to study the relationship between parameters and to compare the results according to the surgeons’ level of experience. RESULTS Nine laparoscopic surgeons participated in this study, five gynecologists and four digestive surgeons. Gynecological surgeons showed a positive correlation between their level of experience, heart rate, hand motion, and GOALS score, except for bimanual dexterity. The reduction in the variability of hand accelerations led to improved tissue handling. Digestive surgeons showed a negative correlation between their level of experience and the reported temporal demand and complexity of the procedure. Novice digestive surgeons reported increased workload during surgery, mainly in mental, physical, and temporal workload. In both surgical specialties, it was observed that reduction in surgical workload was associated with improved GOALS score. CONCLUSIONS Preliminary results showed that bimanual dexterity is independent of the experience of gynecological surgeons. During laparoscopic surgical procedures, reduced variability in hand accelerations leads to better tissue manipulation. In addition, in gynecological and upper gastrointestinal procedures, reduced surgical workload is associated with improved surgical performance.

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.

Effects of pregnancy and chronic exercise on maternal cardiac structure and function

1999 ◽  
Vol 77 (11) ◽  
pp. 909-917 ◽  
Author(s):  
L A Wolfe ◽  
R J Preston ◽  
G W Burggraf ◽  
M J McGrath
Keyword(s):  
Heart Rate ◽  
Resting State ◽  
Cycle Ergometer ◽  
Structure And Function ◽  
Cardiac Structure ◽  
Cycle Ergometer Exercise ◽  
Ergometer Exercise ◽  
Aerobic Conditioning ◽  
Cardiac Structure And Function ◽  
And Function

This study examined the interactive effects of pregnancy and aerobic conditioning on maternal cardiac structure and function. Effects of closely monitored cycle ergometer conditioning were studied during the second (TM2) and third trimesters (TM3) in 22 previously sedentary pregnant women (exercised group, EG) and a nonexercising pregnant control group with similar characteristics (CG, n = 19). Subjects were studied in the resting state by two-dimensional echocardiography and during cycle ergometer exercise at three steady-state power outputs at the start of TM2 (ENTRY), at the end of TM2 and TM3 (postconditioning), and 3-4 months postpartum (NPR, nonpregnant reference, CG only). Aerobic conditioning did not increase left ventricular dimensions beyond those attributable to pregnancy itself. In addition, in contrast with previous studies of nonpregnant women, physical conditioning during pregnancy did not reduce heart rate (HR) in the resting state. During exercise, the slope of the HR versus oxygen uptake (Vo2) regression decreased significantly between preconditioning and the end of TM3 in the EG, suggesting that training-induced reductions in HR become more evident with increasing exercise intensity. Also, significant reductions in oxygen pulse (Vo2/HR) were observed at all three work rates in the CG, but not in the EG. These findings support the hypothesis that the cardiovascular effects of aerobic conditioning are obscured by more powerful effects of pregnancy in the resting state but become "unmasked" during strenuous exercise.Key words: human gestation, cycle ergometer exercise, echocardiography, heart rate, stroke volume.

scholarly journals Implementing Wearable Sensors for Continuous Assessment of Daytime Heart Rate Response in Inpatient Rehabilitation

2018 ◽  
Vol 24 (12) ◽  
pp. 1014-1020 ◽  
Author(s):  
Douglas L. Weeks ◽  
Gina L. Sprint ◽  
Virgeen Stilwill ◽  
Amy Lou Meisen-Vehrs ◽  
Diane J. Cook
Keyword(s):  
Heart Rate ◽  
Heart Rate Response ◽  
Wearable Sensors ◽  
Inpatient Rehabilitation ◽  
Continuous Assessment

A Wireless Body Area Sensor Based IOT Scheme for Healthcare Applications

2021 ◽  
Author(s):  
Anna Palagan C ◽  
Sanjai Gupta ◽  
Anand J Dhas ◽  
Shrikant Taware ◽  
Ravi Chakravarthi R ◽  
...  
Keyword(s):  
Health Care ◽  
Heart Rate ◽  
Psychological Health ◽  
Wireless Sensors ◽  
Base Station ◽  
Health State ◽  
Body Area ◽  
Healthcare Applications ◽  
Speed Up ◽  
Physical And Psychological Health

Abstract Between the collections of applications allowed by the IoT, smart and linked health care may be mainly vital one. Networked sensors, either damaged on body or entrenched in atmospheres, alter the assembly of wealthy info symptomatic of our physical and psychological health. For example, heart patient parameter such as BP, heart rate and activities of fetal to regulate their health state. In this paper, a coordinator node has devoted on patient’s body to gather all the signals from the wireless sensors and directs them to base station. The involved sensors on patient’s body form a WBAN and they are talented to sense the heart rate, BP and so on. This scheme can notice the irregular conditions, problem an alarm to the patient and direct a message to the clinician, ambulance and family. The focal benefit of this scheme in assessment to earlier systems is to decrease the energy consumption to extend the network period, speed up and encompass the statement coverage to upsurge the choice for enhance patient superiority of lifetime. Here, we focus the chances and tasks for WSN in understanding this idea of longer term of health care.

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