Sensing Optimum in the Raw: Leveraging the Raw-Data Imaging Capabilities of Raspberry Pi for Diagnostics Applications

Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-05, Vol.21 (10), p.3552
Main Authors: Tonelli, Alessandro, Mangia, Veronica, Candiani, Alessandro, Pasquali, Francesco, Mangiaracina, Tiziana Jessica, Grazioli, Alessandro, Sozzi, Michele, Gorni, Davide, Bussolati, Simona, Cucinotta, Annamaria, Basini, Giuseppina, Selleri, Stefano
Format: Article
Language:English
Subjects:
Cameras
CMOS
Compensation
Computer industry
Customization
Data acquisition
Data imaging
Experiments
imaging
Laboratories
Linux
Open source software
Oxidative stress
photometric analysis
Programming languages
Prototyping
Raspberry Pi
raw data
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Summary:Single-board computers (SBCs) and microcontroller boards (MCBs) are extensively used nowadays as prototyping platforms to accomplish innovative tasks. Very recently, implementations of these devices for diagnostics applications are rapidly gaining ground for research and educational purposes. Among the available solutions, Raspberry Pi represents one of the most used SBCs. In the present work, two setups based on Raspberry Pi and its CMOS-based camera (a 3D-printed device and an adaptation of a commercial product named We-Lab) were investigated as diagnostic instruments. Different camera elaboration processes were investigated, showing how direct access to the 10-bit raw data acquired from the sensor before downstream imaging processes could be beneficial for photometric applications. The developed solution was successfully applied to the evaluation of the oxidative stress using two commercial kits (d-ROM Fast; PAT). We suggest the analysis of raw data applied to SBC and MCB platforms in order to improve results.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21103552