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In the recent development the Internet of Things (IoT) brings all electronics objects in to a single domain and it is easy to access everything through internet. The applications of IoT are Smart agriculture, Smart Home, Smart City, Smart health monitoring system etc. The automation of health care is one of the application which monitors the patient health status using IoT to make medical equipments more efficient by monitoring the patient's health, in which identifies the body conditions and reduces the human error. A health care monitoring system is used to monitor patient's body parameters for the particular disese and obtain the various values about it. The heart rate monitor is one of the in system using IoT to recognize the cardic patients condition and monitor the status in emergency situations. It monitors the heart rate of the patient with long term cardiovascular disease. Here the Arduino based microcontroller is used to communicate to the sensors such as pulse sensor and ECG Sensor. The system can analyze the signal, extract features from it, detect the normal or abnormal conditions with the help of Raspberry Pi and the results of the ECG signals is sent to the web server. It ensures the signal transmission of heart rate signal to the database through IoT. This also suggests doctors to care the patient follow-up their patient using the patient's data stored in the database. Thus IoT brings one of the solution for cardiac patient monitoring and also reduces the complexity between patient outcome and technology. [ABSTRACT FROM AUTHOR]

The XXI century companies are increasingly interested on in the development of complex processes on bioengineering and health sciences. In this context, it is essential for undergraduate and graduate students to understand the behavior of complex phenomena. However, nowadays, academic programs are limited to the study of linear systems and first-order approximations; while companies use sophisticated software applications applied to complex systems, whose results have to be interpreted. In order to bridge this gap, in this paper we propose a learning methodology based on working with simulation programs of non-linear dynamical systems and chaos to treat complexity, applied to bioengineering. In particular, in this work we make a software application to analyzing three interesting studies in the context of biological systems: a nonlinear electrical oscillator that is a paradigm of self-sustained oscillations, a heartbeat model based on this oscillator, and the Brusselator system to model autocatalytic biochemical reactions. As a result, we are proving how it is possible, with this application, to analyze the complex behavior and the anomalies in biological systems with this methodology. By means of the proposed software applications in this paper, we analyze and facilitate the learning involving models closer to the real world. [ABSTRACT FROM AUTHOR]

Stethoscope is a special device to hear heartbeat sound and monitor pulmonary disease. The most type of stethoscope used these days is the acoustic stethoscope. However, the problem with this acoustic stethoscope is the sound level very low. It is hard to analyze the heart sound and difficult to be diagnosed by a medical doctor. Therefore, this project was developed to monitor and display heartbeat sound using wireless digital stethoscope. The condenser microphone is used as a sensor to capture the low sensitivity of heart sound signal. ZigBee Pro Series 1 wireless module is used to send the heart beat signal wirelessly in 100 meters range. Microcontroller Arduino Nano and Arduino Mega were used as a platform to process the signal and sent the result to the computer. Graphical User Interface (GUI) was developed using MATLAB software to monitor real time electrocardiogram (ECG) waveform and for data logging purpose. The result shows that this device able to transmit and receive ECG waveform wirelessly. The ECG signal can be recorded through data logging application for further analysis by the medical personnel. [ABSTRACT FROM PUBLISHER]

Microwave Doppler radar system is utilized to sense breathing and heartbeat of human beings to monitor their vital conditions. The Doppler radar not only receives vital sign signals but also acquires different additive and unwanted waves such as the signal of the extra motion of the body, clutter and electromagnetic noise. Making a mathematical model of received signal by a Doppler radar will be helpful to analyze vital conditions. In this paper we simulated the signal received by a Doppler radar system, and the results were compared with the vital signals obtained from a human by both a custom designed 24 GHz radar and a commercial respiratory transducer attached around the chest; in both time and frequency domains. [ABSTRACT FROM PUBLISHER]

The vital sign, including respiration and heartbeat signal, can be detected through a non-contact Doppler radar. Since the harmonics and intermodulation products of the respiration are major interferences according to the previous researches, the heartbeat signal cannot be clearly identified without holding the breath, detected from back of the subject or just filtered to remove respiration signal. A synchronized motion technique (SMT) based on Doppler concept is proposed to suppress the interference from the respiration in non-contact heartbeat detection. The experiment results show that the SMT effectively improve the heartbeat-to-respiration ratio (HRR) up to 76 % while subject kept normal breathing. The SMT mechanism is also analyzed through non-linear simulation which agrees well with the measurement data. [ABSTRACT FROM PUBLISHER]

Make the low frequency high energy magnetic field stimulate the hearts of amphibian toad and mammalian rat and rabbit. Compare the different Biological effects of the Magnetic Stimulation on different species of animals. Research shows that the animal hearts contracting amplitude restored after stimulated by the low frequency high energy magnetic field. There is a greater effect on the lower animals. The heart rhythmic pulsatile cycle of toad, rat and rabbit were not change. [ABSTRACT FROM PUBLISHER]

The purpose of this study is to develop a stimulation, which has an active physiological influence to prevent severe driver drowsiness while driving. This paper presents a heartbeat-synchronized vibration, which is the rhythmic pulsation of a motor with each beat of the heart, as an effective means to ease drivers' slight deprivation of oxygen with the appearance of cardiorespiratory coordination. The effects of this stimulation were confirmed by the results of an experiment done in cooperation with 15 subjects. The results showed that cardiorespiratory coordination appeared under the stimulation and it played an active influence on the oxygen supply to the body. We conclude that the vibratory stimulation has an active influence to prevent drowsiness. It might be effective to prevent severe drowsiness while driving. [ABSTRACT FROM PUBLISHER]

There were two key problems in applying Doppler radar to night-time heartbeats and respiration measurement systems. The first is noise associated with body movement and the second is the presence of undesired harmonics and intermodulation. We propose a background subtraction processing and three different methods of combining the radar output spectra. In particular, a method involving real-time selection of the best radar-output channel by comparing the numbers of large peaks in the FFT spectrum, is found to be effective in improving the measurement accuracy. A prototype of the system was set up at a nursing home and field tests were carried out with elderly subjects. Despite the subjects engaging in frequent body movements while sleeping, the system was quite effective in monitoring vital signs (r=0.625–0.812). [ABSTRACT FROM PUBLISHER]

We analyzed heartbeat-intervals with our own program of detrended fluctuation analysis (DFA) to quantify the irregularity of the heartbeat. The present analysis revealed that normal healthy subjects have the scaling exponent of 1.0, and ischemic heart disease pushes the scaling exponent up to 1.2–1.5. We conclude that the scaling exponent, calculated by the DFA, reflects a risk for the “failing” heart. The scaling exponents could determine whether the subjects are under sick or in healthy conditions on the basis of cardiac physiology. [ABSTRACT FROM AUTHOR]