Introduction DAQ and Raspberry Pi

No, raspberry pi isn’t a new flavor of the baked pastry dish delicacy that is awesome on your taste buds but bad for the waistline. Here, we are referring to a simple series of miniature, single-boards computers developed by the Raspberry Pi Foundation and ideal for data acquisition tasks.

The Raspberry Pi computer runs on the Linux, kernel operating system. It is credit-card sized and can be easily plugged into a monitor and keyboard. But don’t let its size fool you, because it is quite capable of punching above its weight, carrying out many similar computing functions of a desktop PC.

However, it is Raspberry Pi’s ability to interact with the outside world rather seamlessly and carry out instructions that make it handy with Data acquisition (DAQ) systems.

DAQ systems include the processes and products that facilitate the gathering of information signals in order to measure and analyze real-life phenomena. DAQ systems are often the focal point in larger ecosystems that integrate a wide variety of other products such as data loggers and sensors.

Because of the array of different products they often employ, there is a huge imperative for data acquisition applications to be cost-effective. Raspberry Pi is a natural ally because it fulfills most of this core need.

Boasting a cost less than $40, it is the ideal product, providing a cost-effective approach to implement DAQ systems. 

Use Cases of Raspberry Pi in DAQ systems

Raspberry Pi-based new power quality (PQ) instruments

Both the use and constant growth of electrical systems have necessitated researchers and engineers to be involved in resolving power quality problems.

In order to ameliorate these problems, power quality (PQ) measurements are utilizing new instruments that deploy the Raspberry Pi as refinements are sought for high accuracy power measurements. 

In this architecture, the personal computer has been substituted with Raspberry Pi, boosting cost benefits that permit the acquired data to be processed locally, then subsequently sent to remote servers to be broadcast widely to users. 

Naturalistic driving studies and automotive Data Acquisition Module (ADAM)

A prototype was built in a study developed to collect acceleration, speed, naturalistic position and headway data by driving researchers. This system was based on two Raspberry Pi microcomputers, and other associated gear such as cameras, and GPS, and IMU sensors. 

The objective of this prototype was to design a low-cost, non-intrusive, portable Automotive Data Acquisition Module (ADAM). The eventual data gathered is a time-series format with the goal of assisting both human factors studies, and constructing driver models for human-in-the-loop control.

The resulting structure can be mounted on the windscreen of a vehicle, presenting no interference to either the safety of the car or the driver. 

Raspberry Pi and Machine Learning

Python has emerged as the premier programming language used to perform data analysis and coding machine learning operations.

Because Raspberry Pi allows to use of modern programming languages such as C —  but more importantly Python, it is finding a new lease of life in machine learning applications. Therefore, Raspberry Pi is on the verge of spreading to process and DAQ applications that require not only acquiring signals but also intricate data analysis.

Using Raspberry Pi to acquire signals comes with other advantages too. Deploying it directly to a sensor or a machine can enable the researcher to gain insight into when breakages in signals occur, thereby anticipate where adjustments are required. They can then use Python’s in-built machine learning its capabilities to improve the overall quality of the plant.

Space weather monitoring

The advantages of Raspberry Pi’s cheapness, nimbleness, and low maintenance are being exploited for a good benefit in space weather research. This is manifest in a weather program’s need to distribute inexpensive ionospheric monitors (SID), which are necessary to detect the changes in the planet’s ionosphere.

Not to get too far out into the technical weeds of weather meteorology, but these ionospheres prove problematic because they “affect very low frequency (VLF) radio propagation.” In 2012, some researchers from a Malaysian university were able to build a VLF receiver system, but they used Raspberry Pi in order to counteract the limiting effects of power consumption, size, and costs of using traditional desktop computers.

In this DAQ structure, the underlying acquisition software was written and compiled in Python, and subsequently, run on a Linux environment.

Using DAQ and Raspberry Pi for biochemical analyzers

The analysis done in biochemical laboratories needs to follow certain procedures. One of these requirements is that high volume analysis results be immediately acquired from analyzers in an instantaneous, automatic manner.

Moreover, these analyzers come from different manufacturers, consisting of various types. This reality, therefore, requires a solution that takes into consideration the heterogeneous and varied nature of laboratory analyzers. 

DAQ solutions using the Raspberry Pi model B and other system frameworks have been developed to save the acquired results from these labs in Electronic Health Records (EHR). By using data acquisition methods, labs are able to avoid errors that would have been made in abundance if manually transcription had been used. 

Because the emphasis was on an inexpensive, cost-effective solution, the Raspberry Pi became an obvious candidate to implement the solution with.

Shortcomings of using Raspberry Pi

While Raspberry Pi has found reliable use in some DAQ applications, there are however mostly limited to those that require only relatively low speeds. Therefore, this makes them precluded from use in high-speed data acquisition systems. 

However, if you are still serious about using Raspberry Pi in high demanding environments, especially mixed signal work, then you probably need to attach it to a more sophisticated, peripheral device.


Raspberry Pi, due to its distinct features of low-cost, programmability, and portability, has been able to carve out a niche for itself in the implementation of data acquisition systems. 

As the system continues to experience ongoing improvement, the range of DAQ applications it will be deployed in is likely to increase. Also, most Raspberry Pi DAQ applications also include support for USB, Ethernet, Bluetooth, and PCI hardware. 

However, as it is currently constituted, it is yet to make a suitable case for implementation in high-speed DAQ systems.