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Development of INNO-MEDU100 Using LabVIEW, Multisim, and myRIO

Nam Sang-Woo, System Integration Dept. Ltd. INNOTMES

"We developed INNO-MEDU100 for the digital healthcare industry using the NI graphical system design platform. It offers efficient and sufficient functionality for many researchers, professors, and students."

- Nam Sang-Woo, System Integration Dept. Ltd. INNOTMES

The Challenge:

We had to develop research and training products that can handle FPGA and real time, and develop a high-speed data acquisition system to measure biosignals by applying a specific algorithm needed to enable the data analysis, and transmit the data acquired to illustrate the concept of a digital healthcare system that provides networking to an external display device.

The Solution:

INNO-MEDU100 is the educational development of a biosignal measurement system kit with biomedical sensors. Four kinds of sensors (ECG, EMG, SpO2, and NIBP) are measuring biological signals, and configured by making use of the signal processing circuit and FPGA-based software so the user can directly develop a system. INNO MEDU100 can be configured with additional sensors to design a circuit on the bread board built.

Ltd. INNOTEMS

INNOTEMS has expertise and knowledge in PC-based control and instrumentation R&D, system inspection and testing, system consulting, automobile, machinery, aviation, aerospace, medical, and education. The company uses system integration services based on LabVIEW software.

 

Introduction

Digital healthcare is a key issue in Korea and worldwide, but there is no research or education system for digital healthcare. We developed a mobile healthcare system with a fully functional electrocardiogram (ECG), electromyograph (EMG), pulse oximeter, and noninvasive blood pressure (NIBP) measurement with elaborate analysis using NI graphical system design.

 

We used our experience and understanding of the students’ development for studying biomedical engineering to create an integrated system with hardware and software that can help students develop a different application system and can be used for individual projects.

 

The INNO-MEDU100 uses a myRIO device and LabVIEW to provide the acquiring human biosignal and analyzing function. Furthermore, we used Multisim to add an amplifier and filters for signal processing for the front end circuit design.

 

The Structure of INNO MEDU100

Students designed the analog signal processing circuit of the sensor to be implemented. And the signal converted analog to digital and displayed the desired
data on the analysis algorithms. We transferred the data through wired/wireless support and it is available on a notebook or tablet PC.

 

Configuration of Analog Signal Processing

The circuit is designed differently depending on the sensor used. By default, it must be designed to amplify and filter according to the characteristics of the biosignal. In addition, it must be designed to pull the plug if in contact with human skin for safety.

 

Configuration of the Software

Each program is coded in the FPGA and real-time applications with myRIO and LabVIEW, depending on the device you want to display on your notebook. Use a tablet PC to configure the front-panel with a data dashboard.

 

Notebooks of the device for displaying the data separated by each of the four sensors were constructed by the icon representative waveform image.

 

Clicking on the icon for each sensor, the subwindow to determine the data, the signal analysis process to the signal is configured.


 

Medical Sensors

 

  • ECG Sensor

ECG signal is a weak electrical activity in the heart of a measurable body surface. Phase electrical activity of the heart is atrial depolarization, ventricular depolarization, divided into ventricular repolarization. Each of these steps is reflected in the form of P, QRS, T wave called a few waves as shown in the following figure.

 

  • EMG Sensor

The surface EMG is measured as analgesia, noninvasive method of attaching the electrodes to the skin surface. There is therefore a needle electromyography to measure only the electrical activity of single muscle motor units can quantitatively analyze the overall synergistic activity of the muscle motor unit assembly comfortable without pain otherwise.

 

 

  • SpO2 Sensor

PPG is to measure a pulse wave signal of the biological signal of a person, the pulse wave signal is a signal to an LED light irradiated on the capillaries of the index finger, for detecting the amount of hemoglobin in the blood is absorbed or transmitted light with a photodiode.

 

  • NIBP Sensor

Blood pressure is the pressure of blood flowing along a blood vessel to the wall of the blood vessel. It is also a principal vital sign. Blood pressure and heart rate varies depending on the systolic and diastolic blood pressure. It is possible to measure the minimum blood pressure, maximum blood pressure, and heart rate.

 

 

Conclusion

  • We developed INNO-MEDU100 for the digital healthcare industry using the NI graphical system design platform. It offers efficient and sufficient functionality for many researchers, professors, and students.
  • INNO-MEDU100 could be downsizing by the built-in myRIO. We could easily access the FPGA and real-time processor. In addition, by using the myRIO WiFi network, we could develop a digital healthcare system training platform.
  • A circuit simulation using Multisim helped us understand the output signal by checking the signal processing to the virtual instruments. Multisim features a variety of virtual instruments (oscilloscope, digital multimeter, power supply, and more) and can run multiple simulations.

 

Using the INNO MEDU100 can easily measure the biosignals, and additional design is possible, if necessary. Simple biological signal measurement as well as the implementation of an embedded system by experiencing the signal analysis deals with the FPGA and real-time helps with the study of a foundation of biomedical engineering knowledge.

 

 

Author Information:

Nam Sang-Woo
System Integration Dept. Ltd. INNOTMES
South Korea
Tel: 042-936-0615
swnam@innotems.com

Figure 2. shows the configuration of the signal processing circuit of the ECG sensor.
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