Customer Solutions

Measuring, Transmitting, and Monitoring Vital Body Signs with Wearable Sensors

Author(s):

Mural Guler, Istanbul Technical University

Industry:

Research

Product:

LabVIEW

The Challenge:

Developing a wireless, wearable vital body sign monitoring system to measure respiration rate and monitor physical activity.

The Solution:

Using National Instruments LabVIEW software and data acquisition hardware with compact, microelectromechanical systems-based (MEMS-based) sensors to develop a garment capable of transferring live data on vital body signs.

With advancements in technology and science, electronics are getting smaller and smaller, which gives researchers and scientists the ability to weave electronics and interconnections into very small places, such as fabrics. This technology has created a new concept called “e-textiles,” which provide cost-effective and efficient solutions for various applications, including medical, athletics, and military projects.

At Istanbul Technical University, we developed an e-textile garment capable of monitoring vital signs during physical activities. The ARF-Smart Shirt system contains two main elements. A mobile portion includes a MEMS-based accelerometer, 5 V power-supply unit, and an RF transmitter. The second, fixed part contains an RF receiver, a connector block, a National Instruments PCI-6024E data acquisition board and a computer with P4 processor.

Initially, our goal was to investigate the possible uses of commercial off-the shelf products, such as MEMS-based sensors, which are very cheap and readily available.

We used a MEMS-based 2-axis accelerometer to detect respiration rate and body posture. The pulse width modulation (PWM) output of the sensor was transmitted with an RF transmitter-receiver pair. The RF transmitter is located on the shirt with a mobile component comprising a sensor, a 9 V battery, and a regulator. The RF receiver relayed the signal to the PCI-6024E. Both counter input and analog input of the PCI-6024E were tested for noise-free, real-time reading. We preferred analog input reading for both graphic-representation and signal-processing purposes. We used fast Fourier transform (FFT) and other NI LabVIEW mathematical tools to analyze the incoming signal in real time. Frequency and amplitude classification gave important information about the subject’s activities in terms of creating visual or sound warning signals.

The produced PWM signals from the MEMS accelerometer were sent to the RF-transmitter module. The accelerometer and RF-transmitter module were supplied with a 5 V direct current, and the required power was obtained from a circuit containing the 9 V battery and an LM7805 regulator. The accelerometer has an approximately 14-bit analog-to-digital converter. The acquired signals from the body did not include high frequencies, so the accelerometer, with its 12.5 percent/g sensitivity was therefore an appropriate sensor to use in this project.

The output of the accelerometer, in the form of a PWM signal, was sent to the PCI-6024E by RF-transmission. The ATX-34 and ARX-34 RF modules transmitted data in the 434 MHz bandwidth range with a 2,400 bps data rate. The digital output of the RF receiver module was connected to the analog input of the PCI-6024E.

This data acquisition board has 16 single-ended analog inputs with 12-bit resolution. The PWM signal was sampled at 200 KHz, and 4,000 samples were acquired. Sampled signals were acquired in LabVIEW, and its duty cycle was measured to calculate the acceleration in real-time.

The ADXL202E MEMS-based accelerometer was placed on the body with its x and y axes parallel to the ground and perpendicular to the human body. After monitoring and logging the acceleration data in real time, we obtained the characteristic amplitude and frequency values for each activity by analyzing the data. We then determined the amplitude and frequency ranges for the classification of respiration rate and body activities such as standing, walking, and running.

With the ARF-Smart Shirt vital body signs monitoring system, activities to measure respiration rate in a stationary position and while walking and running were repeated. The measured amplitude and frequency values of the acceleration-time signal were classified in comparison blocks. The LED indicators showed the results according to the values obtained from the comparison blocks. The boundary values for the respiration rate and body activities determine the reliability of the system. To achieve more reliable results and to determine amplitude and frequency ranges of activities, different measurements for the same activities were repeated using a treadmill with 10 subjects between ages 18 and 25.

Initially, serial communication protocols using C programming and programmable integrated chips (PIC) were used to transmit and process RF signals. This method, however, did not yield good results within a reasonable amount of time. Therefore, we used the PCI-6024E and LabVIEW for initial design and testing. The advanced signal processing features of LabVIEW, such as filtering and FFT, significantly accelerated the prototyping process. After this study, LabVIEW and other NI products are our indispensable choice.