MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Ru, Xu; Gu, Nian; Shang, Huaming; Zhang, Heng

doi:10.3390/mi13060879

Cited by 53 publications

(34 citation statements)

References 102 publications

(120 reference statements)

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“…The results showed a more apparent offset in the sagittal plane for the hip and ankle joints than for the knee joint profile, which may be due to the relatively greater misalignment that occurs in the IMUs, which were fixed at the sacrum and the anterior upper shank during running. Similar to previous studies, our results also suggest that misalignment between IMUs may be the main cause of measurement errors ( Bergmann et al, 2009 ; Ru et al, 2022 ). Compared to retroreflective markers that are usually affixed to the bone landmarks, IMUs positioned in the middle of the body segment which tend to have larger dimensions, making the IMUs more susceptible to soft tissue motion ( Bergmann et al, 2009 ).…”

Section: Discussionsupporting

confidence: 91%

Validity and reliability of inertial measurement units measurements for running kinematics in different foot strike pattern runners

Zeng

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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This study aimed to assess the validity and reliability of the three-dimensional joint kinematic outcomes obtained by the inertial measurement units (IMUs) for runners with rearfoot strike pattern (RFS) and non-rearfoot strike pattern (NRFS). The IMUs system and optical motion capture system were used to simultaneous collect 3D kinematic of lower extremity joint data from participants running at 12 km/h. The joint angle waveforms showed a high correlation between the two systems after the offset correction in the sagittal plane (NRFS: coefficient of multiple correlation (CMC) = 0.924–0.968, root mean square error (RMSE) = 4.6°C–13.7°C; RFS: CMC = 0.930–0.965, RMSE = 3.1°C–7.7°C), but revealed high variability in the frontal and transverse planes (NRFS: CMC = 0.924–0.968, RMSE = 4.6°C–13.7°C; RFS: CMC = 0.930–0.965, RMSE = 3.1°C–7.7°C). The between-rater and between-day reliability were shown to be very good to excellent in the sagittal plane (between-rater: NRFS: CMC = 0.967–0.975, RMSE = 1.9°C–2.9°C, RFS: CMC = 0.922–0.989, RMSE = 1.0°C–2.5°C; between-day: NRFS: CMC = 0.950–0.978, RMSE = 1.6°C–2.7°C, RFS: CMC = 0.920–0.989, RMSE = 1.7°C–2.2°C), whereas the reliability was weak to very good (between-rater: NRFS: CMC = 0.480–0.947, RMSE = 1.1°C–2.7°C, RFS: CMC = 0.646–0.873, RMSE = 0.7°C–2.4°C; between-day: NRFS: CMC = 0.666–0.867, RMSE = 0.7°C–2.8°C, RFS: CMC = 0.321–0.805, RMSE = 0.9°C–5.0°C) in the frontal and transverse planes across all joints in both types of runners. The IMUs system was a feasible tool for measuring lower extremity joint kinematics in the sagittal plane during running, especially for RFS runners. However, the joint kinematics data in frontal and transverse planes derived by the IMUs system need to be used with caution.

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Section: Discussionsupporting

confidence: 91%

Validity and reliability of inertial measurement units measurements for running kinematics in different foot strike pattern runners

Zeng

Liu

et al. 2022

Front. Bioeng. Biotechnol.

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“…In addition, MEMS components usually require sophisticated deep learning models just to recognize the patterns of interest (forward or backward rotation, etc. ), and the results may still contain a large margin of uncertainty which must be mitigated by complicated calibration methods compared to the simple calibration used for the capacitive touch sensor [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

TactCube: An Intelligent Device to ‘converse’ with Smart Environments

Battistoni

Gregorio

Romano

et al. 2022

Sensors

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Ambient Intelligence is a vision of daily life in which intelligent devices interact with humans to make their lives easier, and the technology is invisible. Artificial Intelligence (AI) governs this smart environment and must interact with humans to best meet their needs and demands. Although voice assistants are very popular and efficient as conversational AI, under some conditions they cannot be used. Therefore, this work proposed a complementary tactile and tangible interface to converse with AI, creating new Tactile Signs. A prototype of TactCube, a wireless cube-shaped device that can interact with AI using only the tactile sense, is presented. The hypothesis is that TactCube can be manipulated with one hand and generate a sequence of numbers that can be interpreted as a new tactile language by a neural network solution. The paper describes the initial research made to define how these sequences can be generated and demonstrates how TactCube is able to do it.

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“…As shown in Figure 1d, when the triaxial accelerometer is installed in an inclinometer, the three sensitive axes should be aligned with the three orthogonal axes of the body frame. In practice, however, misalignment errors occur due to errors in the sensor mounting process [31,32]. To ensure that the orthogonal output readings are displayed correctly with the sensor frame by the nonorthogonal triaxial accelerometer, misalignment errors 𝜃, 𝜑, and 𝜓 must be estimated.…”

Section: Sensor Frame Error Modelmentioning

confidence: 99%

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Yuan¹,

Tang²,

Zhang³

et al. 2022

Preprint

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This paper presents a calibration and thermal compensation method that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized, and then, based on this optimized calibration number, thermal compensation is performed, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results have been obtained nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of -40°C to 60°C. The temperature drifts of x- and y-axes are reduced from -13.2 and 11.8 mg to -0.9 and -1.1 mg, respectively. The z-axis temperature drift is reduced from -17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. Therefore, the MEMS sensors calibrated in this paper have certain engineering application values for tilt measurement.

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MEMS Inertial Sensor Calibration Technology: Current Status and Future Trends

Cited by 53 publications

References 102 publications

Validity and reliability of inertial measurement units measurements for running kinematics in different foot strike pattern runners

Validity and reliability of inertial measurement units measurements for running kinematics in different foot strike pattern runners

TactCube: An Intelligent Device to ‘converse’ with Smart Environments

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

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