Abstract
This study focuses on assessing the reliability of data from the accelerometer sensors embedded in smart mobile devices that may potentially be used for robotics and intelligent transportation systems (ITS) applications. It is shown how bias and noise elimination can be executed more consistently from acceleration profiles obtained from an accelerometer with a high amount of error. In cases where accurate acceleration information could not be detected through noise filtering, averaged acceleration values in specific time windows were computed and introduced as measurement values to the filtering algorithm. Thus, more consistent acceleration profiles were obtained through making better state estimations. As an alternative to one dimensional bias elimination process, bias errors were detected in six different orientations in three dimensions and subtracted from raw readings. Furthermore, ratiometricity analysis, which is important in applications that require long-term data collection, but is generally overlooked, was also performed through collecting continuous acceleration data for twelve hours. Ratiometric error was numerically quantified and completely subtracted from the raw data through computation of slopes between specific error regions with linear variation assumption between these regions.
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Bakirci, M., Cetin, M. (2023). Reliability of MEMS Accelerometers Embedded in Smart Mobile Devices for Robotics Applications. In: GarcÃa Márquez, F.P., Jamil, A., Eken, S., Hameed, A.A. (eds) Computational Intelligence, Data Analytics and Applications. ICCIDA 2022. Lecture Notes in Networks and Systems, vol 643. Springer, Cham. https://doi.org/10.1007/978-3-031-27099-4_7
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