“Real-time monitoring of heart rate by processing of Microsoft Kinect™ 2.0 generated streams”

2016 ◽  
Author(s):  
Ilaria Bosi ◽  
Chiara Cogerino ◽  
Marco Bazzani
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Microsoft Kinect ◽  
Real Time Monitoring

scholarly journals Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Piezoelectric Ceramic ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
High Heart Rate ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

scholarly journals Two Distinct Types of Sweat Profile in Healthy Subjects While Exercising at Constant Power Output Measured by a Wearable Sweat Sensor

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Dong-Hoon Choi ◽  
Grant Kitchen ◽  
Ji Soo Kim ◽  
Yi Li ◽  
Kain Kim ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Healthy Subjects ◽  
Perceived Exertion ◽  
Chloride Concentration ◽  
Rating Of Perceived Exertion ◽  
Real Time Monitoring ◽  
Sweat Chloride

AbstractWearable sweat sensors have enabled real-time monitoring of sweat profiles (sweat concentration versus time) and could enable monitoring of electrolyte loss during exercise or for individuals working in extreme environments. To assess the feasibility of using a wearable sweat chloride sensor for real-time monitoring of individuals during exercise, we recorded and analyzed the sweat profiles of 50 healthy subjects while spinning at 75 Watts for 1 hour. The measured sweat chloride concentrations were in the range from 2.9–34 mM. The sweat profiles showed two distinct sweat responses: Type 1 (single plateau) and Type 2 (multiple plateaus). Subjects with Type 2 profiles had higher sweat chloride concentration and weight loss, higher maximum heart rate, and larger changes in heart rate and rating of perceived exertion during the trial compared to subjects with Type 1 profiles. To assess the influence of level of effort, we recorded sweat profiles for five subjects at 75 W, 100 W, and 125 W. While all five subjects showed Type 1 sweat profiles at 75 W, four of the subjects had Type 2 profiles at 125 W, showing an increase in sweat chloride with exercise intensity. Finally, we show that sweat profiles along with other physiological parameters can be used to predict fluid loss.

scholarly journals Smart Bed Health Monitoring based on ECG and Body Temperature using Lab View and IoT

2019 ◽  
Vol 9 (2) ◽  
pp. 4631-4647
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Real Time ◽  
Health Monitoring ◽  
Influential Factors ◽  
Real Time Monitoring ◽  
Long Distance ◽  
Labview Software ◽  
Heart Monitoring ◽  
Monitoring Devices

Heart disease remains as the major cause of death encompassed by Malaysians over the past ten years starting from 2005 until 2014. There are many factors that influenced this statistical measurement. One of the most influential factors is due to insufficient space for medical placement provided by the hospitals. The purpose of this study is to develop a smart bed health monitoring system based on electrocardiography (ECG) and body temperature sensors. Arduino IDE software was used for Internet of Things (IoT) and LabVIEW software for real time monitoring. Data can be seen through a website, which can help the doctors to monitor their patients’ data from a long distance. The result of the project was compared with existing monitoring devices used for heart rate (Hand Wrist Heart Monitoring and Galaxy Watch) and body temperature (Rossmax Monitoring TG380 Thermometer) which available in the market. These comparisons were conducted by several experiments to analyze its accuracy and reliability. The comparison of data between real time monitoring and IoT was analyzed to check for the effectiveness of data via internet. The result showed that the highest percentage of error for both parameters of heart rate and body temperature were less than 4.2%. This system able to interpret an individual’s level of healthiness such as bradycardia, tachycardia and fever which can be monitored in real time monitoring.

scholarly journals A Flexible Wireless Sensor Patch for Real-Time Monitoring of Heart Rate and Body Temperature

2019 ◽  
Vol E102.D (5) ◽  
pp. 1115-1118 ◽  
Author(s):  
Seok-Oh YUN ◽  
Jung Hoon LEE ◽  
Jin LEE ◽  
Choul-Young KIM
Keyword(s):  
Heart Rate ◽  
Body Temperature ◽  
Real Time ◽  
Wireless Sensor ◽  
Real Time Monitoring

scholarly journals An Internet of Medical Things-Based Model for Real-Time Monitoring and Averting Stroke Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hatim Z. Almarzouki ◽  
Hemaid Alsulami ◽  
Ali Rizwan ◽  
Mohammed S. Basingab ◽  
Hatim Bukhari ◽  
...  
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Healthcare Management ◽  
Relative Power ◽  
Medical Monitoring ◽  
Real Time Monitoring ◽  
Structured Interviews ◽  
Opposite Pattern ◽  
Mean Values ◽  
Acquisition Processes

In recent years, neurological diseases have become a standout amongst all the other diseases and are the most important reasons for mortality and morbidity all over the world. The current study’s aim is to conduct a pilot study for testing the prototype of the designed glove-wearable technology that could detect and analyze the heart rate and EEG for better management and avoiding stroke consequences. The qualitative, clinical experimental method of assessment was explored by incorporating use of an IoT-based real-time assessing medical glove that was designed using heart rate-based and EEG-based sensors. We conducted structured interviews with 90 patients, and the results of the interviews were analyzed by using the Barthel index and were grouped accordingly. Overall, the proportion of patients who followed proper daily heart rate recording behavior went from 46.9% in the first month of the trial to 78.2% after 3–10 months of the interventions. Meanwhile, the percentage of individuals having an irregular heart rate fell from 19.5% in the first month of the trial to 9.1% after 3–10 months of intervention research. In T5, we found that delta relative power decreased by 12.1% and 5.8% compared with baseline at 3 and at 6 months and an average increase was 24.3 ± 0.08. Beta-1 remained relatively steady, while theta relative power grew by 7% and alpha relative power increased by 31%. The T1 hemisphere had greater mean values of delta and theta relative power than the T5 hemisphere. For alpha ( p  < 0.05) and beta relative power, the opposite pattern was seen. The distinction was statistically significant for delta ( p  < 0.001), alpha ( p  < 0.01), and beta-1 ( p  < 0.05) among T1 and T5 patient groups. In conclusion, our single center-based study found that such IoT-based real-time medical monitoring devices significantly reduce the complexity of real-time monitoring and data acquisition processes for a healthcare provider and thus provide better healthcare management. The emergence of significant risks and controlling mechanisms can be improved by boosting the awareness. Furthermore, it identifies the high-risk factors besides facilitating the prevention of strokes. The EEG-based brain-computer interface has a promising future in upcoming years to avert DALY.

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