Analysis, modeling and compensation of bias drift in MEMS inertial sensors

Gulmammadov, Farid

doi:10.1109/rast.2009.5158260

Cited by 34 publications

(14 citation statements)

References 7 publications

(10 reference statements)

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“…First, raw data obtained from the gyroscope and accelerometer sensors were filtered with moving average using a window of 100 samples. This was performed to remove the bias drift of inertial sensors [34].…”

Section: Feature Extraction Methodsmentioning

confidence: 99%

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering From Double Trans-Femoral Amputation

Chereshnev

Kertész‐Farkas

2018

Preprint

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Several studies have analyzed human gait data obtained from inertial gyroscope and accelerometer sensors mounted on different parts of the body. In this article, we take a step further in gait analysis and provide a methodology for predicting the movements of the missing parts of the legs. In particular, we propose a method, called GaIn, to control non-invasive, robotic, prosthetic legs. GaIn can infer the movements of both missing shanks and feet for humans suffering from double trans-femoral amputation using biologically inspired recurrent neural networks. Predictions are performed for casual walking related activities such as walking, taking stairs, and running based on thigh movement. In our experimental tests, GaIn achieved a 4.55 degree prediction error for shank movements on average. However, a patient's intention to stand up and sit down cannot be inferred from thigh movements. In fact, intention causes thigh movements while the shanks and feet remain roughly still. The GaIn system can be triggered by thigh muscle activities measured with electromyography (EMG) sensors to make robotic prosthetic legs perform standing up and sitting down actions. The GaIn system has low prediction latency and is fast and computationally inexpensive to be deployed on mobile platforms and portable devices.

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Section: Feature Extraction Methodsmentioning

confidence: 99%

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering From Double Trans-Femoral Amputation

Chereshnev

Kertész‐Farkas

2018

Preprint

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“…Bias instability in inertial sensors is primarily caused by low-frequency flicker noise in the electronics and temperature fluctuations [ 27 ]. If the bias drift is significant enough that it needs to be compensated for, there are methods that model the biases as time or temperature dependent, enabling continuous estimation of drifting biases (see, e.g., [ 28 , 29 ]). Such methods can be used in combination with the method proposed in this paper.…”

Section: Inertial Measurement Modelsmentioning

confidence: 99%

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Olsson

Kok

Seel

et al. 2020

Sensors

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Inertial motion capture relies on accurate sensor-to-segment calibration. When two segments are connected by a hinge joint, for example in human knee or finger joints as well as in many robotic limbs, then the joint axis vector must be identified in the intrinsic sensor coordinate systems. Methods for estimating the joint axis using accelerations and angular rates of arbitrary motion have been proposed, but the user must perform sufficiently informative motion in a predefined initial time window to accomplish complete identifiability. Another drawback of state of the art methods is that the user has no way of knowing if the calibration was successful or not. To achieve plug-and-play calibration, it is therefore important that 1) sufficiently informative data can be extracted even if large portions of the data set consist of non-informative motions, and 2) the user knows when the calibration has reached a sufficient level of accuracy. In the current paper, we propose a novel method that achieves both of these goals. The method combines acceleration- and angular rate information and finds a globally optimal estimate of the joint axis. Methods for sample selection, that overcome the limitation of a dedicated initial calibration time window, are proposed. The sample selection allows estimation to be performed using only a small subset of samples from a larger data set as it deselects non-informative and redundant measurements. Finally, an uncertainty quantification method that assures validity of the estimated joint axis parameters, is proposed. Experimental validation of the method is provided using a mechanical joint performing a large range of motions. Angular errors in the order of 2 ∘ were achieved using 125–1000 selected samples. The proposed method is the first truly plug-and-play method that overcome the need for a specific calibration phase and, regardless of the user’s motions, it provides an accurate estimate of the joint axis as soon as possible.

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“…A global analysis on the model of errors in MEMS accelerometers can be found in (11) , while a more detailed modeling of their measurement noise is reported in (12) , where the use of Allan's variance is clearly described. Finally, a comprehensive analysis on the bias in the measurements has been conducted in (13) . Here, it can be seen that the bias has by far a more complex description than a stochastic process, being related also to operating temperature.…”

Section: Kalman Estimator Utilizing a Mems Accelerometermentioning

confidence: 99%

Use of MEMS Inertial Sensors for Performance Improvement of Low-cost Motion Control Systems

2016

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The performance of a motion control system depends on hardware capabilities, such as sensor resolution and actuator technology. For a certain plant, an amelioration in performance can often be achieved by using better sensors. This however may require a redesign of the system, as the new sensor may not fit into the existing plant. As for the actuators, many motion control systems using stepper motors exist; these stepper motors known for their high torque ripple and limited dynamic performance, especially in the presence of mechanical resonances of the driven load. A possible solution is to replace the actuator with a better one; however, this again may require a costly redesign of the plant. To improve the performance of existing plants with minimal invasive modifications, our group has developed a set of solutions, based on the use of low-cost MEMS inertial sensors, which can be easily placed on the system to be controlled. We will show the manner in which we used such sensors to develop new control strategies, to enhance the performance of existing sensors and actuators, without major modifications to the plant. This will be demonstrated through some examples taken from both laboratory and industrial applications.

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Analysis, modeling and compensation of bias drift in MEMS inertial sensors

Cited by 34 publications

References 7 publications

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering From Double Trans-Femoral Amputation

GaIn: Human Gait Inference for Lower Limbic Prostheses for Patients Suffering From Double Trans-Femoral Amputation

Robust Plug-and-Play Joint Axis Estimation Using Inertial Sensors

Use of MEMS Inertial Sensors for Performance Improvement of Low-cost Motion Control Systems

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