Managing the sampling rate variability of digital MEMS accelerometers in dynamic calibration
Open accessAbstract
The use of sensors with digital interface, within large or dense sensor networks, is nowadays widespread in many scientific and technological applications: from more traditional applications, such as infrastructure monitoring and predictive maintenance, up to the advanced ones, such as smart manufacturing, IoT, Machine Learning, and other emerging fields of fast-real-time interconnections, within the framework of digitalization. The technical and functional performance of these sensing infrastructures, if accurately identified, allows to enhance the trustworthiness, safety, and accuracy of the managed processes; based on metrological characterizations and calibration, it is possible to provide the actual sensitivity of digital sensors with respect to reference physical stimuli, within the proper uncertainty budgets and suitable covering factors. At present days, metrological characterization and proper calibration of digital sensors is still a technical and methodological challenge and several studies are oriented along with this perspective. In this paper, the sampling rate variability - depending on MEMS analog-to-digital converter, external microcontroller internal clock and their interaction - of digital MEMS accelerometers in dynamic calibration is investigated. The sampling rate variability is evaluated among 25 sensors of the same batch, and within every single sensor in time, and methods to manage the associated uncertainty and to avoid mismatches in calibration, are proposed and discussed.
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D'Emilia et al. found significant variability in actual sampling rates among 25 MEMS-based sensors from the same production batch and noticeable deviations from the nominal rate specified by the manufacturer.
“The results revealed significant variability in the actual sampling rates among the sensors, as well as noticeable deviations from the nominal rate specified by the manufacturer.”
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Prato, A., Mazzoleni, F., & Schiavi, A. (2020) · Metrologia · doi:10.1088/1681-7575/ab79be · Cited by 42 · see citing works
Self-citationGaletto, M., Schiavi, A., Genta, G., et al. (2019) · CIRP Annals · doi:10.1016/j.cirp.2019.04.097 · Cited by 35 · see citing works
Self-citationD’Emilia, G., Gaspari, A., Mazzoleni, F., et al. (2018) · Journal of sensors and sensor systems · doi:10.5194/jsss-7-245-2018 · Cited by 30 · see citing works
Self-citationMetrology for the factory of the future: towards a case study in condition monitoring
Dorst, T., Ludwig, B., Eichstädt, S., et al. (2019) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2019.8826973 · Cited by 26 · see citing works
D’Emilia, G., Gaspari, A., Mazzoleni, F., et al. (2018) · Journal of sensors and sensor systems · doi:10.5194/jsss-7-403-2018 · Cited by 24 · see citing works
Self-citationPrato, A., Mazzoleni, F., & Schiavi, A. (2019) · doi:10.1109/metroi4.2019.8792906 · Cited by 21 · see citing works
Self-citationMetrological Management of Large-Scale Measuring Systems
Carullo, A. (2006) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2006.870125 · Cited by 21 · see citing works
Amplitude–phase calibration of tri-axial accelerometers in the low-frequency range by a LDV
D’Emilia, G., Gaspari, A., & Natale, E. (2019) · Journal of sensors and sensor systems · doi:10.5194/jsss-8-223-2019 · Cited by 18 · see citing works
Self-citationCalibration of digital 3-axis MEMS accelerometers: A double-blind «multi-bilateral» comparison
Schiavi, A., Prato, A., Mazzoleni, F., et al. (2020) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot48571.2020.9138215 · Cited by 15 · see citing works
Self-citationPrato, A., Mazzoleni, F., & Schiavi, A. (2020) · ACTA IMEKO · doi:10.21014/acta_imeko.v9i5.1007 · Cited by 13 · see citing works
Self-citationD’Emilia, G., Gasbarro, D. D., Gaspari, A., et al. (2017) · Journal of Physics Conference Series · doi:10.1088/1742-6596/882/1/012006 · Cited by 8 · see citing works
Self-citationPrato, A., Schiavi, A., Mazzoleni, F., et al. (2020) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot48571.2020.9138293 · Cited by 8 · see citing works
Self-citationMethods for dynamic calibration and augmentation of digital acceleration MEMS sensors.
Seeger, B., Bruns, T., & Eichstädt, S. (2019) · International Congress of Metrology (CIM) · doi:10.1051/metrology/201922003 · Cited by 7 · see citing works
Sensors with Digital Output – A Metrological Challenge
Mende, M., & Begoff, P. (2019) · International Congress of Metrology (CIM) · doi:10.1051/metrology/201922002 · Cited by 7 · see citing works
Design of metrology equipment running management system based on the internet of things technology
Xu, B. P. (2017) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi.2017.8265781 · Cited by 4 · see citing works
D’Emilia, G., Gaspari, A., & Natale, E. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1149/1/012018 · Cited by 3 · see citing works
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Brambilla, M., Chiariotti, P., & Cigada, A. (2025) · Sensors · doi:10.3390/s25165044 · Cited by 3 · see citing works
“Each digital sensor node consists of a digital accelerometer module based on micro-electro-mechanical system (MEMS) technology, paired with a micro-controller unit (MCU) [6,8].”
- Authors
- D'Emilia, G., Gaspari, A., Natale, E., Prato, A., Mazzoleni, F., & Schiavi, A.
- Affiliations
- Polytechnic University of Bari (IT)
- University of L'Aquila (IT)
- Istituto Nazionale di Ricerca Metrologica (IT)
- Venue
- 2021 IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT)
- Publisher
- IEEE
- Published
- 2021-06-07
- Pages
- 687-692
- OpenAlex
- W3184387690
- Cited by
- 8
- References
- 16
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 4
- FWCI
- 3.19
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- Locations
- IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) (publishedVersion)
- CINECA IRIS Institutional Research Information System (IRIS Istituto Nazionale di Ricerca Metrologica) (submittedVersion, other-oa)
- Archivio Istituzionale della Ricerca - Università degli Studi dell Aquila (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Scientific Measurement and Uncertainty Evaluation
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Computer Science › Computer Networks and Communications › Sensor Technology and Measurement Systems
- Keywords
- dynamic calibration, analog-to-digital converter, measurement uncertainty, sensor networks
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