Magnetic Measurement Units Research Articles

Inside-Out Magnetic Tracking for Virtual/Augmented Reality Applications

High definition 6 degrees of freedom (6-DoF) tracking in the Personal Area Tracking (PAT) region offers a wide range of applications. In this region, inside-out trackers can potentially offer the advantage of being portable. Outside-in trackers, though offering superior performance on average, lack this portability which might limit the number of PAT applications. Thus, there is a need for developing inside-out trackers that can achieve comparable performance. To that end, a tracking system based on AC magnetic field and inertial measurement units (IMUs) has been developed. It consists of a transmitter that can be integrated into a headset and multiple wireless trackers. The tracking system is compact, portable, wireless and low power consuming. The system is optimized to minimize jitter and latency, which are crucial for providing a good user experience in Virtual/Augmented Reality (VR/AR) applications. The compact design is compatible with mobile computing units such as a smartphone. This work presents the challenges associated with the magnetic field symmetry that hinders 8-octant tracking. A method using motion correlation is implemented to perform 8-octant tracking. A position jitter of less than 1 mm up to 1 m, an update rate of 400 Hz, and a latency of about 5 ms have been achieved through sensor fusion, on-the-fly calibration, and filtering.

An Innovative Sensor Fusion Algorithm for Motion Tracking With On-Line Bias Compensation: Application to Joint Angles Estimation in Yoga

Magnetic Inertial Measurement Units (MIMUs) represent an increasingly used technology in the field of human motion. However, their applications are strongly affected by magnetic disturbances and poor calibration, which could determine inaccurate attitude estimations. Thus, Inertial Measurement Units (IMUs), relying exclusively on accelerometers and gyroscopes, could be used as an alternative to MIMUs for motion tracking, avoiding the negative effect of magnetic disturbances. Unfortunately, gyroscope signals are characterized by several error sources, among which gyroscope bias, whose variations due to environmental factors and temporal instability strongly affect the attitude estimation accuracy. Aim of the present work is to propose a novel sensor fusion algorithm for IMU-based applications that embodies an adaptive on-line bias capture module. The accuracy of the proposed filter was tested on ten expert yoga practitioners during the execution of a sun salutation sequence. The right upper limb joint angles were estimated and their accuracy assessed in terms of Mean Absolute Error (MAE) and Pearson’s correlation coefficient by comparison with an optoelectronic motion capture system. The achieved worst-case accuracy was 5.59, 6.75 and 3.49 degrees for the wrist, elbow and shoulder joints respectively. The accuracy of the algorithm is further confirmed by the high values of the Pearson’s correlation coefficients between IMUs and OMC angles, which were greater than 0.91, 0.92 and 0.98 respectively for the three considered joints. The proposed algorithm can thus be considered as a promising tool for attitude estimation in those contexts when magnetic interferences cannot be monitored and, thus, adequately compensated.

Hand-Finger Pose Estimation Using Inertial Sensors, Magnetic Sensors and a Magnet

Hand-finger motion tracking based on inertial and magnetic measurement units (IMMUs) has become a well-accepted method in many applications such as rehabilitation and virtual reality. However, existing systems require an IMMU on each segment of the whole hand and known length of each segment, which is hard to achieve for users. Therefore, we proposed a small sensor configuration with three IMMUs and a magnet to estimate poses of interested fingertips relative to the hand. The relative orientations were estimated by integrating relative angular velocity and fused with relative orientation estimation during time intervals when the whole hand moved or rotated as one object. The relative positions were estimated by using a magnetic dipole model of the magnet and prior estimated relative orientations. The performance has been demonstrated in multiple experiments. For whole hand rotation and functional grasping or writing experiments, the estimated errors of index fingertip position and orientation relative to the hand were 8.0~9.8 mm and 5.7° ~11.2°. For index finger flexion and pinching, the estimated errors of index fingertip position and orientation relative to the hand were 16.2~25.2 mm and 20.6° ~ 21.7°.

Sensor fusion algorithms for orientation tracking via magnetic and inertial measurement units: An experimental comparison survey

Lightweight and low-cost wearable magnetic and inertial measurement units (MIMUs) have found numerous applications, such as aerial vehicle navigation or human motion analysis, where the 3D orientation tracking of a rigid body is of interest. However, due to the errors in measurements of gyroscope, accelerometer, and/or magnetometer inside a MIMU, numerous studies have proposed sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by performing an experimental comparison among a variety of SFAs. Notably, we compared the estimated orientation of 36 SFAs from the complementary filter and linear/extended/complementary/unscented/cubature Kalman filter families with the reference orientation obtained from a camera motion-capture system. The experimental study included data collection with a foot-worn MIMU where nine participants performed various short- and long-duration tasks. We shared the codes and sample of data in https://www.ncbl.ualberta.ca/codes to enable other researchers to compare their works with the literature toward creating a comprehensive online repository for SFAs. To perform a fair comparison, we used the Particle Swarm Optimization routine to find the optimal adaptive gain tuning scheme for each SFAs, as recommended in the literature. Our experimental results showed that gyroscope static bias removal, in general, showed to be effective in reducing the estimation error of SFAs, specifically during long-duration trials. Moreover, our experimental results identified the SFAs with the highest accuracy from each family. We also reported the execution times for the selected SFAs from each family. This paper is among the first experimental comparison studies which provide such breadth of coverage across various SFAs for tracking orientation with MIMUs.

Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study.

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

Respiratory synchrony comparison between preterm and full-term neonates using inertial sensors.

Due to inefficient respiratory control, newborns become prone to asynchronous thoracoabdominal (TA) movements. The present study quantitatively estimated the synchrony of TA in preterm and full-term newborns through an inertial and magnetic measurement units (IMMUs) system. This cross-sectional study was conducted with 20 newborns divided into Preterm Group (PTG, n = 10) and Full-Term Group (FTG, n = 10). Each neonate had IMMUs placed on the sternum and near the umbilicus, thus the TA motion was estimated through the resultant inclination angles calculated using a sensor fusion filter. The respiratory incursions were also manually counted and video-recorded for two minutes, then used to validate a Matlab custom-written routine for their automatic identification. The respiratory cycles were used to calculate the phase change angle (φ) between the thoracic and abdominal compartments. Association between the manual and automatic methods were verified by Pearson's correlation and root mean squared errors (RMSE), and the comparison between the groups was performed through the Student's t test with α = .05. The values of respiratory incursions measured by both methods showed a high association and low measurement error (r = .96, RMSE = 9.8, p < .001). The FTG presented a higher occurrence of TA synchrony (p = .049) while the PTG group presented a higher occurrence of TA asynchrony (p = .036). No difference was found between the groups regarding the paradoxical classification (p = .071). The proposed method was valid to quantitatively assess the TA synchrony of hospitalized neonates. Preterm infants had a higher occurrence of the asynchronous respiratory pattern in comparison to full-term infants.

Orientation Estimation by Partial-State Updating Kalman Filter and Vectorial Magnetic Interference Detection

Attitude estimation based on a magnetic and inertial measurement unit is popular in the areas of robotics, aerospace, and unmanned aerial vehicles. Nevertheless, the magnetometer is prone to external magnetic disturbance that deteriorates the state estimation. This article proposes a novel partial state update strategy in the extended Kalman filter (PS-EKF) by optimally solving the posteriori covariance optimization problem under the Kalman gain constraint, so as to obviate the magnetic measurement update on two level angles and the inertial sensor biases. Besides, a new magnetic disturbance detection technique (vectorial detector) considering both the norm and the direction of the magnetometer measurement is proposed. Simulations and experiments show that the proposed PS-EKF method can be totally immune to the magnetic interference on the level angles and sensors biases, in contrast to the traditional extended Kalman filter using either the direct magnetic output or the indirect yaw as the measurement.

Simultaneous End User Calibration of Multiple Magnetic Inertial Measurement Units With Associated Uncertainty

A fast method to simultaneously calibrate multiple MEMS Magnetic Inertial Measurement Units (MIMUs) accurately in the field is needed in many application areas. The MEMS MIMUs require calibration of systematic errors of bias, sensitivity, non-orthogonality and misalignment, which vary with temperature and use. Even after calibration, the sensors undergo stochastic errors in static and dynamic conditions and thus uncertainty of output must also be modeled. We propose a method for easy and fast calibration of multiple MIMUs together, while mounted on a single platform. The precise alignment of sensors is not assumed. Our method calibrates both fixed array of MEMS MIMUs or many independent MIMUs simultaneously using kinematic constraints. The novelty of our approach is that the uncertainty of sensors output is also learned as part of our model. Compared with existing state-of-art methods, our algorithm gives more consistent readings of all MIMUs and our framework also predicts the associated uncertainty of the sensor output. The uncertainty prediction of individual sensors is particularly helpful in the sensor fusion.

A Full-State Robust Extended Kalman Filter for Orientation Tracking During Long-Duration Dynamic Tasks Using Magnetic and Inertial Measurement Units.

Accurate and robust orientation estimation using magnetic and inertial measurement units (MIMUs) has been a challenge for many years in long-duration measurements of joint angles and pedestrian dead-reckoning systems and has limited several real-world applications of MIMUs. Thus, this research aimed at developing a full-state Robust Extended Kalman Filter (REKF) for accurate and robust orientation tracking with MIMUs, particularly during long-duration dynamic tasks. First, we structured a novel EKF by including the orientation quaternion, non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector. Next, the a posteriori error covariance matrix equation was modified to build a REKF. We compared the accuracy and robustness of our proposed REKF with four filters from the literature using optimal filter gains. We measured the thigh, shank, and foot orientation of nine participants while performing short- and long-duration tasks using MIMUs and a camera motion-capture system. REKF outperformed the filters from literature significantly (p < 0.05) in terms of accuracy and robustness for long-duration tasks. For example, for foot MIMU, the median RMSE of (roll, pitch, yaw) were (6.5, 5.5, 7.8) and (22.8, 23.9, 25) deg for REKF and the best filter from the literature, respectively. For short-duration trials, REKF achieved significantly (p < 0.05) better or similar performance compared to the literature. We concluded that including non-gravitational acceleration, gyroscope bias, and magnetic disturbance in the state vector, as well as using a robust filter structure, is required for accurate and robust orientation tracking, at least in long-duration tasks.

Estimate Hand–Finger Position With One Magnetometer and Known Relative Orientation

Inertial-sensor-based hand motion tracking has become a well-accepted method in many clinical applications, including rehabilitation of the upper extremity. However, major drawbacks are that a sensor on each segment and kinematic chain rule is required. Thus, errors accumulate through the kinematic chain rule. Also, the length of each segment needs to be measured for each user. We propose a novel method in which a permanent magnet on the dorsal side of the hand combined with an inertial and magnetic measurement unit (IMMU) on the fingertip is used to estimate the position of the thumb and index fingertip relative to the position of the dorsal side of the hand. The biggest advantages of the proposed method are that no prior information and kinematic chain rule are needed. Besides, the required number of sensors is low. A Levenberg–Marquardt (L-M) approach is presented to estimate the relative positions with the known orientations. The performance is demonstrated in multiple experiments in which various movement tasks were performed. The most complex task in which participants performed reaching and grasping movements based on the action research arm test (ARAT) resulted in a median distance error between thumb and index finger of 9.6%.

Magnetometer Robust Deep Human Pose Regression With Uncertainty Prediction Using Sparse Body Worn Magnetic Inertial Measurement Units

We propose a deep learning based framework that learns data-driven temporal priors to perform 3D human pose estimation from six body worn Magnetic Inertial Measurement units sensors. Our work estimates 3D human pose with associated uncertainty from sparse body worn sensors. We derive and implement a 3D angle representation that eliminates yaw angle (or magnetometer dependence) and show that 3D human pose is still obtained from this reduced representation, but with enhanced uncertainty. We do not use kinematic acceleration as input and show that it improves the generalization to real sensor data from different subjects as well as accuracy. Our framework is based on Bi-directional recurrent autoencoder. A sliding window is used at inference time, instead of full sequence (offline mode). The major contribution of our research is that 3D human pose is predicted from sparse sensors with a well calibrated uncertainty which is correlated with ambiguity and actual errors. We have demonstrated our results on two real sensor datasets; DIP-IMU and Total capture and have come up with state-of-art accuracy. Our work confirms that the main limitation of sparse sensor based 3D human pose prediction is the lack of temporal priors. Therefore fine-tuning on a small synthetic training set of target domain, improves the accuracy.

A Simplified Extended Kalman Filter Used for Magnetic and Inertial Measurement Units

As a sensor unit, a magnetic and inertial measurement unit (MIMU) is mainly composed of a tri-axis gyroscope, a tri-axis accelerometer and a tri-axis magnetometer. The Extended Kalman Filter (EKF) is the currently preferred algorithm used to fuse the outputs of the MIMU sensor in order to determine the attitude of the MIMU's carrier platform. One of the main problems of the EKF is its high computational cost, which is largely due to solving the matrix inversion in the Kalman gain. This article simplifies the Kalman gain by ensuring it does not contain any matrices that would require an inversion operation. This is achieved by simplifying the linearized measurement matrix (the Jacobian matrix) through orthogonalizing the observation and reference vectors. The inversion of the matrix contained in the Kalman gain can then be mathematically derived and the derived matrix can eventually replace the matrix inversion, in order to avoid the inversion operation when the EKF is executed. The simplified EKF is compared to the traditional EKF experimentally and the results show that the simplified method has similar accuracy to the traditional method, but with a faster execution time.

Effect of Functional Electrical Stimulation of the Gluteus Medius during Gait in Patients following a Stroke.

Many stroke patients rely on cane or ankle-foot orthosis during gait rehabilitation. The purpose of this study was to investigate the immediate effect of functional electrical stimulation (FES) to the gluteus medius (GMed) and tibialis anterior (TA) on gait performance in stroke patients, including those who needed assistive devices. Fourteen stroke patients were enrolled in this study (mean poststroke duration: 194.9 ± 189.6 d; mean age: 72.8 ± 10.7 y). Participants walked 14 m at a comfortable velocity with and without FES to the GMed and TA. After an adaptation period, lower-limb motion was measured using magnetic inertial measurement units attached to the pelvis and the lower limb of the affected side. Motion range of angle of the affected thigh and shank segments in the sagittal plane, motion range of the affected hip and knee extension-flexion angle, step time, and stride time were calculated from inertial measurement units during the middle ten walking strides. Gait velocity, cadence, and stride length were also calculated. These gait indicators, both with and without FES, were compared. Gait velocity was significantly faster with FES (p = 0.035). Similarly, stride length and motion range of the shank of the affected side were significantly greater with FES (stride length: p = 0.018; motion range of the shank: p = 0.026). Meanwhile, cadence showed no significant difference (p = 0.238) in gait with or without FES. Similarly, range of motion of the affected hip joint, knee joint, and thigh did not differ significantly depending on FES condition (p = 0.115‐0.529). FES to the GMed and TA during gait produced an improvement in gait velocity, stride length, and motion range of the shank. Our results will allow therapists to use FES on stroke patients with varying conditions.

Full-pose estimation using inertial and magnetic sensor fusion in structurized magnetic field for hand motion tracking

The individual differences and flexible muscular tissue upon bone make it difficult to realize high accuracy hand motion tracking. A full-pose estimation method is proposed by fusing measurements from an Inertial and Magnetic Measurement Unit (IMMU) in structurized magnetic field derived from a marked permanent magnet. Rather than eliminating the non-geomagnetic field, we realize spatial hand motion estimation (including both the location and orientation) by introducing structurized magnetic field to provide increased physical field dimensions without involving field coupling. The IMMU orientation is derived by using the pose matrix expressed by quaternion with complementary filter. Then, 3D position is localized by solving the magnetic inverse problem with derived orientation. Experiments on a developed platform show that the mean individual orientation error of roll, pitch and yaw attitude angle is 0.61°, 0.52° and 0.92°, respectively. In continuous path tracking, the mean localization error and mean orientation error around Z-axis motion and around X-axis motion is (1.56 mm, 0.12°) and (1.2 mm, 0.10°), respectively. The mean full-pose estimation process time is 1.6 ms. This method provides high-precision full-pose estimation for hand gesture recognition.

40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges

Technological developments over the past two decades have resulted in the development of more accurate and lightweight low-cost magnetic and inertial measurement units (MIMUs). These developments have allowed the extensive application of MIMUs in various fields, specifically tracking the 3D orientation of a rigid body. Despite recent technological improvements, measurements from a tri-axial gyroscope, accelerometer, and/or magnetometer inside the MIMU are characterized by uncertainties. Numerous studies have been conducted to address these uncertainties and develop sensor fusion algorithms (SFAs) to estimate the 3D orientation accurately and robustly. This paper contributes to these efforts by providing a survey of the state-of-the-art SFAs for orientation estimation. We surveyed +250 publications, categorized the SFAs with various structures, identified the modifications proposed to improve their performance, and discussed the strengths and weaknesses of these approaches. We found that, while early SFAs were mostly a vector observation algorithm or an extended Kalman filter, to improve the computational efficiency, more recent works have developed SFAs with a complementary filter or complementary Kalman filter structure. At the same time, to improve the performance of the SFAs, several research teams have proposed various modifications to the basic structure of these filters, such as adaptive gain tuning or imperfect measurement rejection. We also provided an outlook on the lessons learned as well as the main challenges related to SFAs and discussed the practical steps toward developing an effective SFA. We have identified the need for benchmarking studies as the main challenge at the moment. This paper is among the first surveys which provide such breadth of coverage across different SFAs for tracking orientation with MIMUs.

Adaptive Gain Regulation of Sensor Fusion Algorithms for Orientation Estimation with Magnetic and Inertial Measurement Units

Magnetic and inertial measurement units (MIMUs) proved to be an accurate alternative for optic, electromagnetic, or acoustic measurement technologies. While the orientation of the MIMU could be estimated using accelerometer, gyroscope, or magnetometer sensors alone, many studies proposed sensor fusion algorithms (SFAs) to overcome the drawbacks that appear when each sensor is used individually. However, the performance of such SFAs highly depends on their gains, and poor initialization or incorrect adjustment of the gains would degrade the SFAs’ performance. Therefore, this article proposes a general framework to find the optimal adaptive gain tuning scheme for Kalman filters and complementary filters to achieve accurate and robust orientation estimation with MIMUs. To this end, we proposed an innovative optimization framework to find the fixed optimal gain of an SFA or the optimal adaptive gain regulation scheme. Also, we demonstrated that the designed adaptive gain regulation scheme (a hard switch with two or three levels or a fuzzy inference system) is essential for orientation tracking with various SFAs. We measured the thigh, shank, and foot motion of nine participants while performing various activities using MIMUs and a camera motion-capture system to calculate the MIMUs’ error in 3-D angle estimations. Gain regulation by hard switch was significantly ( $p ) more accurate and robust than it was for innovation adaptive estimation. Also, for all tested SFAs, hard switching for shank and foot MIMUs was significantly more accurate or robust than that for fixed optimal gain. Our experimental results showed that the adaptive gain tuning of SFAs using optimized gains is crucial, regardless of the algorithm structure or complexity.

Conditional equivalence between Extended Kalman filter and complementary filter for two-vector gyro-aided attitude determination

Extended Kalman Filter (EKF) and Complementary Filter (CF) are the two most commonly-used attitude determination algorithms in inertial and magnetic measurement units. It is known that the only difference between the a posterior attitude estimates provided by EKF and CF respectively is the gain matrix (GM) assigned to innovation for attitude update. Through mathematical derivation, it is concluded that the GM of EKF can be simplified to the one of CF, which means that CF is an approximation to EKF. Monte Carlo simulations were done to validate what influence of these simplifications is put on the performance of EKF. The main finding is EKF is more stable than CF in condition of low sampling rate, and hence CF must rely more on the measurements of gyroscope for attitude determination to improve its stability.

Changes In Running Mechanics During A Typical Interval Training On The Track Measured With IMUs

An 8 x 400 meter interval training is often performed with the aim of improving aerobic fitness. Besides being physiological and physically challenging this type of training is also mechanically demanding. As such it could have potential negative effects on running mechanics. Inertial magnetic measurement units (IMUs) allow for continuous measurement of running mechanics during this type of training. PURPOSE: To investigate changes in running mechanics during an 8 x 400 meter interval training on the athletic track using IMUs. METHODS: 5 trained athletes (4M 1F, 25.4±7.9 years, 185.6±8.3 cm, 69.2±12.7 kg) ran 8 x 400 meters on the athletic track. They were paced to run each 400 meter at 5 km race pace with half of the time run as rest. Eight IMUs (240 Hz) were placed at the feet, tibia, upper legs, sacrum and sternum. Accelerometer data and sensor orientation were used to calculate the following parameters using custom code after calibration trials: Hip, knee and ankle angle at Initial Contact (IC), knee angle at Midstance (MST) and Midswing (MSW), peak tibial and sacral acceleration (PTA, PSA), and centre of mass (COM) displacement. Parameters were calculated for both straights of the 2nd, 4th, 6th and 8th 400 meters. Borg scale (0-10) was asked after every bout for perceived exertion. Paired sample t-tests were used to test for statistical differences between the 2nd and 8th bout. RESULTS: Table 1 CONCLUSIONS: Running mechanics (mainly ankle and knee mechanics and tibial impact) changed over the course of a typical interval training, putting runners at higher risk with increasing bouts. This indicates that this type of training is not only physiological and physically demanding but puts increasing mechanical stress on the body. These results suggest caution should be used among athletes returning from an overuse injury.

Constraint-augmented Kalman Filter for Magnetometer-free 3D Joint Angle Determination

Accurate three-dimensional (3D) intersegment joint angle measurement is an important biomechanical measure for a variety of applications. Although inertial and magnetic measurement units (IMMUs) are mobile alternatives to optical motion capture systems, the joint angle determination accuracy based on the IMMU is highly vulnerable to the magnetic distortion problem associated with magnetometer signals. This paper proposes a constraint-augmented Kalman filter (KF) for magnetometer-free 3D joint angle determination using inertial measurement unit (IMU) signals. The proposed KF uses two linear KFs consisting of a previously developed roll/pitch KF followed by a newly formulated yaw KF. In the yaw KF, the ball joint constraint provides a measurement equation for the yaw correction procedure, whereas magnetometers accomplish this procedure in most standalone algorithms. In the yaw KF, the yaw angle itself is not designated as a state because it is difficult to develop the equation in conjunction with the trigonometric functions. Instead, the sine and cosine of the yaw angle are designated as states in the form of algebraic variables. As the proposed approach does not rely on the magnetometer signals even for the 3D joint angle, it is completely free from the magnetic distortion problem. Experimental results utilizing a two-link mechanism connected by a ball joint verified that augmentation of the constraint successfully performed the role of correction. Accordingly, in magnetically undesirable conditions, the proposed IMU-based approach produced higher accuracy than the IMMU-based approaches. Furthermore, this paper investigated the residual of the constraint to provide an insight into how well each approach satisfies the kinematic constraint.

Inertial motion capture validation of 3D knee kinematics at various gait speed on the treadmill with a double-pose calibration

BackgroundInertial motion capture (IMC) is rapidly gaining in popularity to evaluate gait in clinical settings. Previous examinations of IMC knee kinematics were often limited to the sagittal plane and IMC calibration has not been thoroughly investigated. Research questionThe objective was to validate IMC 3D knee kinematics calibrated with a double-pose during gait with reference to optical motion capture (OMC). The hypotheses are that IMC can estimate adequately knee kinematics and that both systems will detect similarly the changes with gait speed. MethodsTwenty-four healthy participants walked on the treadmill at gait speed of 0.6, 0.8, 1.0 and 1.2 m/s. Knee kinematics were obtained simultaneously with two magnetic and inertial measurement units and passive markers fixed on the KneeKG system. OMC was calibrated with a functional anatomical approach and the IMC with a double-pose. ResultsRoot mean square differences of the two systems yielded 3-6° for knee flexion, adduction and external rotation. Knee kinematics were more similar during the stance phase than the swing phase. Gait speed showed a significant progressive effect on the three knee angles that was similarly detected by the two systems. SignificanceIMC 3D knee kinematics can be obtained independently with a simple calibration and only two magnetic and inertial measurement units at an acceptable level of error especially during stance.