Back in 2012, I wrote about signal conditioning for MEMS and sensors. Amplifiers and architectures have not changed very much since, but there are a few newer devices and design architectural techniques that may be better for MEMS and sensor conditioning in some design cases. I will be including not only analog operational amplifier solutions in this article, but discrete transistor, data converter, microcontroller, and algorithm-based solutions as well. The following are some key applications that use these different architectural sensor conditioning techniques and some of the latest products in interesting topic areas.

Medical: Monitoring sounds in our bodies1
Monitoring sounds in the human body is still an important means of medical diagnosis for doctors. Back in the 19th century, auscultatory1 (from Latin auscultatus, "to listen attentively to") technology was first used in stethoscopes. Technology has advanced beyond a microphone-type of sensor to piezoelectric sensors in modern day technology.

Most of today’s electronic stethoscopes are designed with a set of configurable filters that have a different frequency response. These filters enable better listening in various areas of the human body, such as the heart (20Hz to 400Hz range), joints, intestines, or the lungs (100Hz to 1200Hz range). Most of these filters are designed as a band-pass with a tunable cut-off frequency. Noise reduction algorithms are frequently employed to reduce interference such as patient movement or ambient noise. Maxim Integrated has a nice block diagram of an electronic stethoscope (Figure 1).


Figure 1 Block diagram of a digital stethoscope (Image courtesy of Maxim Integrated)


Stethoscopes can also have mechanically-tunable diaphragms to condition the signal. See this 3M Littmann website and video.

Medical: Wireless ECG for mobile health monitoring2
A wireless wearable device, addressed in Reference 2, is able to measure electrocardiogram (ECG) and respiration rate (RR) through the use of non-contact capacitive-based electrodes. A good analog front-end design is the key architecture element in this design that does the signal conditioning and produces a strong, clean output. Embedded into a wearable vest, active electrodes come in contact with the subject’s chest; a reference electrode is placed directly on the subject’s skin. That reference electrode relays a common-mode input signal back to the subject’s skin, which is the system ground in this architecture. Once the signal has been acquired by the electrodes it is then sent to a differential separation filter (DSF). The DSF is responsible for separating the differential signal into two main signal components:

  1. signals with a frequency above 1 Hz
  2. signals with frequencies below 1 Hz (This stage is essential for separating ECG and RR signal components).

The entire signal conditioning circuit can be seen in Figure 2. See Reference 2 for more details about this circuit and the gain and component values used in it.


Figure 2 Analog front end block diagram (Image courtesy of Reference 2)


The instrumentation amplifiers (IAs) in Figure 2 will now begin extracting the ECG signal. High common mode rejection ratio (CMRR) IAs will reject common-mode signals between the two electrodes, thus removing induced noise due to contact or AC interference. INA121s from Texas Instruments (formerly Burr-Brown devices) are FET input devices with high impedance and will amplify the tiny ECG signals. The majority of the system gain will be via the INA121 in order to maximize the CMRR right at the input of the conditioning circuit.

Next, the second-order active low-pass filter (LPF) for the ECG signal has a 100 Hz relatively steep cutoff, which is configured as a Sallen-Key KRC architecture. This is followed by a non-inverting gain of 2 stage and then the necessary anti-aliasing filter in front of the ADC. More details about this AFE and the respiration rate, with similar signal conditioning, and the differential separation filter can be found in Reference 2.

[Continue reading on EDN US: Signal conditioning with chopper amplifiers for MEMS transducers]


References

  1. A novel approach in auscultation technology—new sensors and algorithms, D. Grochala, M. Kajor, D. Kucharski, M. Iwaniec, E. Kantoch, AGH University of Science and Technology, IEEE 2018
  2. Wireless Capacitive-Based ECG Sensing for Feature Extraction and Mobile Health Monitoring, Johan Wannenburg, Reza Malekian , and Gerhard P. Hancke, IEEE Sensors Journal, VOL. 18, NO. 14, JULY 15, 2018
  3. Signal conditioning circuit with ultra-high sensitivity and ultra-low power consumption for MEMS, Parisa Vejdani, Anoir Bouchami, and Frederic Nabki, IEEE 2017
  4. A Guide to Using FETS for Sensor Applications, Ron Quan, Linear Integrated Systems
  5. Ultra-high sensitivity, low-power, dual chopper signal conditioning circuit for integrated sensors, P. Vejdani, K. Allidina, and F. Nabki, 14th IEEE NEWCAS Conference, 2016, pp. 1-4.
  6. An auto-balancing signal conditioning scheme for non-contact measurement of conductivity of water, Tejaswini K. K., Boby George, Jagadeesh Kumar V., R. Srinivasan, and Tata Sudhakar, IEEE 2018
  7. Buffered ADC Family Eliminates Signal Conditioning Complexity, Joe Sousa, Andrew Thomas, Clement Wagner, Mark Thoren, Linear Technology (now part of Analog Devices, Inc.)
  8. Signal conditioning for electrochemical sensors, AN4348, ST Microelectronics
  9. ADuCM355 data sheet, Precision Analog Microcontroller with Chemical Sensor Interface, Analog Devices
  10. An Introduction to Electrochemical Impedance Spectroscopy, C.Reece, Jefferson Lab, 2015

For more in-depth information on applying the Raspberry Pi in commercial applications, check out these other articles in this AspenCore Special Project: