Inertial Measurement Unit Alignment Research Articles

Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.

Using inertial measurement units (IMUs) to estimate lower limb joint kinematics and kinetics can provide valuable information for disease diagnosis and rehabilitation assessment. To estimate gait parameters using IMUs, model-based filtering approaches have been proposed, such as the Kalman filter and complementary filter. However, these methods require special calibration and alignment of IMUs. The development of deep learning algorithms has facilitated the application of IMUs in biomechanics as it does not require particular calibration and alignment procedures of IMUs in use. To estimate hip/knee/ankle joint angles and moments in the sagittal plane, a subject-independent temporal convolutional neural network-bidirectional long short-term memory network (TCN-BiLSTM) model was proposed using three IMUs. A public benchmark dataset containing the most representative locomotive activities in daily life was used to train and evaluate the TCN-BiLSTM model. The mean Pearson correlation coefficient of joint angles and moments estimated by the proposed model reached 0.92 and 0.87, respectively. This indicates that the TCN-BiLSTM model can effectively estimate joint angles and moments in multiple scenarios, demonstrating its potential for application in clinical and daily life scenarios.

A methodology for out of position occupant identification from pressure sensors embedded in a vehicle seat

The airbag deployment against an out of position (OP) occupant is critical. The OP condition can be hardly expected during design, while a too close airbag deployment can cause serious injuries instead of mitigating the crash effects. An adaptive airbag system would be capable to adjust its deployment to the inside scenario. However, the integration between human passengers and intelligent vehicle requires the airbag control unit to be aware of the actual occupant(s) position. In the present research, a methodology for monitoring the occupant(s) position is developed and tested with a seat prototype. A layout of thin film sensors monitors the interface pressure between the occupant and the seat cover. An inertial measurement unit (IMU) monitors the accelerations of the vehicle, considered the reference moving platform. A microcontroller is programmed for pressure sensors calibration, IMU alignment with the vehicle reference system, signals processing, OP detection, and identification. Real driving experiments on a race track were performed in the correct position and in three different OPs. The comparison of the pressure center in longitudinal and transversal directions with the vehicle acceleration enables to identify the OPs.

A New Continuous Rotation IMU Alignment Algorithm Based on Stochastic Modeling for Cost Effective North-Finding Applications

Based on stochastic modeling of Coriolis vibration gyros by the Allan variance technique, this paper discusses Angle Random Walk (ARW), Rate Random Walk (RRW) and Markov process gyroscope noises which have significant impacts on the North-finding accuracy. A new continuous rotation alignment algorithm for a Coriolis vibration gyroscope Inertial Measurement Unit (IMU) is proposed in this paper, in which the extended observation equations are used for the Kalman filter to enhance the estimation of gyro drift errors, thus improving the north-finding accuracy. Theoretical and numerical comparisons between the proposed algorithm and the traditional ones are presented. The experimental results show that the new continuous rotation alignment algorithm using the extended observation equations in the Kalman filter is more efficient than the traditional two-position alignment method. Using Coriolis vibration gyros with bias instability of 0.1°/h, a north-finding accuracy of 0.1° (1σ) is achieved by the new continuous rotation alignment algorithm, compared with 0.6° (1σ) north-finding accuracy for the two-position alignment and 1° (1σ) for the fixed-position alignment.

Novel approach to GPS/SINS integration for IMU alignment

Strapdown inertial navigation system (SINS) requires an initialization process that establishes the relationship between the body frame and the local geographic reference. This process, called alignment, is generally used to estimate the initial attitude angles. This is possible because an accurate determination of the inertial measurement unit (IMU) motion is available based on the measurement obtained from global position system (GPS). But the update frequency of GPS is much lower than SINS. Due to the non-synchronous data streams from GPS and SINS, the initial attitude angles may not be computed accurately enough. In addition, the estimated initial attitude angles may have relatively large uncertainties that can affect the accuracy of other navigation parameters. This paper presents an effective approach of matching the velocities which are provided by GPS and SINS. In this approach, a digital high-pass filter, which implements a pre-filtering scheme of the measured signal, is used to filter the Schuler cycle of discrete velocity difference between the SINS and GPS. Simulation results show that this approach improves the accuracy greatly and makes the convergence time satisfy the required accuracy.

Shuttle Orbiter stellar-inertial reference system

The Space Shuttle stellar-inertial reference system is a velocity and attitude data source during flight operations. An overview of the reference system is presented as well as specifics discussing design concepts, functional operation, and performance capabilities. Techniques for star sighting and inertial measurement unit alignment and calibration are described, and alignment accuracy, star tracker capability, and gyro and accelerometer accuracy are discussed, with emphasis on flight test results. Test programs have confirmed that the system meets performance requirements such as being accurate to within 0.26 degree at the 400,000 foot altitude entry interface in order to execute an accurate touchdown, as well as demonstrating reusability, payload capability, and operational flexibility. Growth possibilities, such as the implementation of rendezvous target tracking, are discussed.