Dynamic accuracy of inertial measurement units during simple pendulum motion

A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3 degrees root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5 degrees and 11.7 degrees depending on the length and type of pendulum swing. The maximum orientation error was greater than 30 degrees , occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8 degrees and 1.3 degrees . Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a 'best case' scenario.     

The static accuracy and calibration of inertial measurement units for 3D orientation

Inertial measurement units (IMUs) are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. Wearable motion capture systems based on IMUs have been advertised as alternatives to optical motion capture. In this paper, the accuracy of five different IMUs of the same type in measuring 3D orientation in static situations, as well as the calibration of the accelerometers and magnetometers within the IMUs, has been investigated. The maximum absolute static orientation error was 5.2°, higher than the 1° claimed by the vendor. If the IMUs are re-calibrated at the time of measurement with the re-calibration procedure described in this paper, it is possible to obtain an error of less than 1°, in agreement with the vendor's specifications (XSens Technologies B.V. 2005. Motion tracker technical documentation Mtx-B. Version 1.03. Available from: www.xsens.com).

The new calibration appears to be valid for at least 22 days providing the sensor is not exposed to high impacts. However, if several sensors are ‘daisy chained’ together changes to the magnetometer bias can cause heading errors of up to 15°. The results demonstrate the non-linear relationship between the vendor's orthogonality claim of < 0.1° and the accuracy of 3D orientation obtained from factory calibrated IMUs in static situations. The authors hypothesise that the high magnetic dip (64°) in our laboratory may have exacerbated the errors reported. For biomechanical research, small relative movements of a body segment from a calibrated position are likely to be more accurate than large scale global motion that may have an error of up to 9.8°.     

Dynamic accuracy of inertial measurement units during simple pendulum motion

A motion measurement system based on inertial measurement units (IMUs) has been suggested as an alternative to contemporary video motion capture. This paper reports an investigation into the accuracy of IMUs in estimating 3D orientation during simple pendulum motion. The IMU vendor's (XSens Technologies) accuracy claim of 3° root mean squared (RMS) error is tested. IMUs are integrated electronic devices that contain accelerometers, magnetometers and gyroscopes. The motion of a pendulum swing was measured using both IMUs and video motion capture as a reference. The IMU raw data were processed by the Kalman filter algorithm supplied by the vendor and a custom fusion algorithm developed by the authors. The IMU measurement of pendulum motion using the vendor's Kalman filter algorithm did not compare well with the video motion capture with a RMS error of between 8.5° and 11.7° depending on the length and type of pendulum swing. The maximum orientation error was greater than 30°, occurring approximately eight seconds into the motion. The custom fusion algorithm estimation of orientation compared well with the video motion capture with a RMS error of between 0.8° and 1.3°. Future research should concentrate on developing a general purpose fusion algorithm and vendors of IMUs should provide details about the errors to be expected in different measurement situations, not just those in a ‘best case’ scenario.     

Functional calibration procedure for 3D knee joint angle description using inertial sensors

Measurement of three-dimensional (3D) knee joint angle outside a laboratory is of benefit in clinical examination and therapeutic treatment comparison. Although several motion capture devices exist, there is a need for an ambulatory system that could be used in routine practice. Up-to-date, inertial measurement units (IMUs) have proven to be suitable for unconstrained measurement of knee joint differential orientation. Nevertheless, this differential orientation should be converted into three reliable and clinically interpretable angles. Thus, the aim of this study was to propose a new calibration procedure adapted for the joint coordinate system (JCS), which required only IMUs data. The repeatability of the calibration procedure, as well as the errors in the measurement of 3D knee angle during gait in comparison to a reference system were assessed on eight healthy subjects. The new procedure relying on active and passive movements reported a high repeatability of the mean values (offset<1°) and angular patterns (SD<0.3° and CMC>0.9). In comparison to the reference system, this functional procedure showed high precision (SD<2° and CC>0.75) and moderate accuracy (between 4.0° and 8.1°) for the three knee angle. The combination of the inertial-based system with the functional calibration procedure proposed here resulted in a promising tool for the measurement of 3D knee joint angle. Moreover, this method could be adapted to measure other complex joint, such as ankle or elbow.     

Functional limits of agreement applied as a novel method comparison tool for accuracy and precision of inertial measurement unit derived displacement of the distal limb in horses

Over ground motion analysis in horses is limited by a small number of strides and restraints of the indoor gait laboratory. Inertial measurement units (IMUs) are transforming the knowledge of human motion and objective clinical assessment through the opportunity to obtain clinically relevant data under various conditions. When using IMUs on the limbs of horses to determine local position estimates, conditions with high dynamic range of both accelerations and rotational velocities prove particularly challenging. Here we apply traditional method agreement and suggest a novel method of functional data analysis to compare motion capture with IMUs placed over the fetlock joint in seven horses. We demonstrate acceptable accuracy and precision at less than or equal to 5% of the range of motion for detection of distal limb mounted cranio-caudal and vertical position. We do not recommend the use of the latero-medial position estimate of the distal metacarpus/metatarsus during walk where the average error is 10% and the maximum error 111% of the range. We also show that functional data analysis and functional limits of agreement are sensitive methods for comparison of cyclical data and could be applied to differentiate changes in gait for individuals across time and conditions.     

Evaluation and calibration of an electromagnetic tracking device for biomechanical analysis of lifting tasks.

Electromagnetic motion tracking devices are increasingly used as a kinematic measuring tool. The aim of this study was to evaluate a long-range transmitter in an environment with a conventional force plate present in order to assess its suitability for further biomechanical applications. Using a calibration apparatus developed in our lab and Optotrack measurements, the performances of the Motion Star were evaluated. Positions and orientations were measured in a 140 x 80 x 120 cm(3) space centered on the force plate. Using a mathematical model developed at Queen's University, these data were calibrated. Errors on position and orientation were less than 150 mm and 10 degrees before calibration of the Motion Star, and less than 20mm and 2 degrees after calibration, with no differences between data collected with the force plate switched on/off. These errors did not depend on sensor orientation. Variability of the signal was small indicating minimal noise. Field distortion was the largest source of measurement error, which increased with the distance between the transmitter and the sensor and the proximity of the sensor to the force plate. Before its use for biomechanical analysis of lifting tasks and validation of dynamic models using force plate data, the data from electromagnetic motion tracking devices must be calibrated to decrease the errors due to electromagnetic field distortion.     

Ambulatory measurement of 3D knee joint angle

Three-dimensional measurement of joint motion is a promising tool for clinical evaluation and therapeutic treatment comparisons. Although many devices exist for joints kinematics assessment, there is a need for a system that could be used in routine practice. Such a system should be accurate, ambulatory, and easy to use. The combination of gyroscopes and accelerometers (i.e., inertial measurement unit) has proven to be suitable for unrestrained measurement of orientation during a short period of time (i.e., few minutes). However, due to their inability to detect horizontal reference, inertial-based systems generally fail to measure differential orientation, a prerequisite for computing the three-dimentional knee joint angle recommended by the Internal Society of Biomechanics (ISB). A simple method based on a leg movement is proposed here to align two inertial measurement units fixed on the thigh and shank segments. Based on the combination of the former alignment and a fusion algorithm, the three-dimensional knee joint angle is measured and compared with a magnetic motion capture system during walking. The proposed system is suitable to measure the absolute knee flexion/extension and abduction/adduction angles with mean (SD) offset errors of -1 degree (1 degree ) and 0 degrees (0.6 degrees ) and mean (SD) root mean square (RMS) errors of 1.5 degrees (0.4 degrees ) and 1.7 degrees (0.5 degrees ). The system is also suitable for the relative measurement of knee internal/external rotation (mean (SD) offset error of 3.4 degrees (2.7 degrees )) with a mean (SD) RMS error of 1.6 degrees (0.5 degrees ). The method described in this paper can be easily adapted in order to measure other joint angular displacements such as elbow or ankle.     

Design and demonstration of a new instrumented spatial linkage for use in a dynamic environment: application to measurement of ankle rotations during snowboarding

Joint injuries during sporting activities might be reduced by understanding the extent of the dynamic motion of joints prone to injury during maneuvers performed in the field. Because instrumented spatial linkages (ISLs) have been widely used to measure joint motion, it would be useful to extend the functionality of an ISL to measure joint motion in a dynamic environment. The objectives of the work reported by this paper were to (i) design and construct an ISL that will measure dynamic joint motion in a field environment, (ii) calibrate the ISL and quantify its static measurement error, (iii) quantify dynamic measurement error due to external acceleration, and (iv) measure ankle joint complex rotation during snowboarding maneuvers performed on a snow slope. An "elbow-type" ISL was designed to measure ankle joint complex rotation throughout its range (+/-30 deg for flexion/extension, +/-15 deg for internal/external rotation, and +/-15 deg for inversion/eversion). The ISL was calibrated with a custom six degree-of-freedom calibration device generally useful for calibrating ISLs, and static measurement errors of the ISL also were evaluated. Root-mean-squared errors (RMSEs) were 0.59 deg for orientation (1.7% full scale) and 1.00 mm for position (1.7% full scale). A custom dynamic fixture allowed external accelerations (5 g, 0-50 Hz) to be applied to the ISL in each of three linear directions. Maximum measurement deviations due to external acceleration were 0.05 deg in orientation and 0.10 mm in position, which were negligible in comparison to the static errors. The full functionality of the ISL for measuring joint motion in a field environment was demonstrated by measuring rotations of the ankle joint complex during snowboarding maneuvers performed on a snow slope.     

Influence of IMU position and orientation placement errors on ground reaction force estimation

Wearable inertial measurement units (IMU) have been proposed to estimate GRF outside of specialized laboratories, however the precise influence of sensor placement error on accuracy is unknown. We investigated the influence of IMU position and orientation placement errors on GRF estimation accuracy. Methods: Kinematic data from twelve healthy subjects based on marker trajectories were used to simulate 1848 combinations of sensor position placement errors (range ± 100 mm) and orientation placement errors (range ± 25°) across eight body segments (trunk, pelvis, left/right thighs, left/right shanks, and left/right feet) during normal walking trials for baseline cases when a single sensor was misplaced and for the extreme cases when all sensors were simultaneously misplaced. Three machine learning algorithms were used to estimate GRF for each placement error condition and compared with the no placement error condition to evaluate performance. Results: Position placement errors for a single misplaced IMU reduced vertical GRF (VGRF), medio-lateral GRF (MLGRF), and anterior-posterior GRF (APGRF) estimation accuracy by up to 1.1%, 2.0%, and 0.9%, respectively and for all eight simultaneously misplaced IMUs by up to 4.9%, 6.0%, and 4.3%, respectively. Orientation placement errors for a single misplaced IMU reduced VGRF, MLGRF, and APGRF estimation accuracy by up to 4.8%, 7.3%, and 1.5%, respectively and for all eight simultaneously misplaced IMUs by up to 20.8%, 23.4%, and 12.3%, respectively. Conclusion: IMU sensor misplacement, particularly orientation placement errors, can significantly reduce GRF estimation accuracy and thus measures should be taken to account for placement errors in implementations of GRF estimation via wearable IMUs.     

An effortless procedure to align the local frame of an inertial measurement unit to the local frame of another motion capture system

Inertial measurement units (IMUs) offer great opportunities to analyze segmental and joints kinematics. When combined with another motion capture system (MCS), for example, to validate new IMU-based applications or to develop mixed systems, it is necessary to align the local frame of the IMU sensors to the local frame of the MCS. Currently, all alignment methods use landmarks on the IMU's casing. Therefore, they can only be used with well-documented IMUs and they are prone to error when the IMU's casing is small. This study proposes an effortless procedure to align the local frame of any IMU to the local frame of any other MCS able to measure the orientation of its local frame. The general concept of this method is to derive the gyroscopic angles for both devices during an alignment movement, and then to use an optimization algorithm to calculate the alignment matrix between both local frames. The alignment movement consists of rotations around three more or less orthogonal axes and it can easily be performed by hands. To test the alignment procedure, an IMU and a magnetic marker were attached to a plate, and 20 alignment movements were recorded. The maximum errors of alignment (accuracy±precision) were 1.02°±0.32° and simulations showed that the method was robust against noise that typically affect IMUs. In conclusion, this study describes an efficient alignment procedure that is quick and easy to perform, and that does not require any alignment device or any knowledge about the IMU casing.     

Calibration of position and angular data from a magnetic tracking device

This paper describes a method for calibrating data from a magnetic tracking device. Position and orientation data were collected in a 1. 6x0.8x1.4m(3) volume using a Polhemus Fastrak((R)) in conjunction with both a long-range and standard transmitter. Position and orientation data were calibrated using a locally linear model based on the position of the measurement. After calibration, the average position and angular errors were less than 1.8cm and 1.2 degrees up to 1.8m from the transmitter for the long-range transmitter. For the standard transmitter, even after calibration, errors increased sharply when the sensor was more than 1.2m from the transmitter. Up to that distance, post-calibration errors were less than 1.2cm and 1. 2 degrees, while up to 1.8m they were below 5cm and 4 degrees. These errors could be further reduced by noise filtering. However, use of the standard transmitter is not recommended at distance greater than 1.2m due to orientation-based effects. It was concluded that for the volume investigated, tracking devices could provide similar three-dimensional accuracy to video systems.     

Validation of a model-based inverse kinematics approach based on wearable inertial sensors

Abstract

Wearable inertial measurement units (IMUs) are a promising solution to human motion estimation. Using IMUs 3D orientations, a model-driven inverse kinematics methodology to estimate joint angles is presented. Estimated joint angles were validated against encoder-measured kinematics (robot) and against marker-based kinematics (passive mechanism). Results are promising, with RMS angular errors respectively lower than 3 and 6?deg over a minimum range of motion of 50?deg (robot) and 160?deg (passive mechanism). Moreover, a noise robustness analysis revealed that the model-driven approach reduces the effects of experimental noises, making the proposed technique particularly suitable for application in human motion analysis.     

Accuracy of an optical active-marker system to track the relative motion of rigid bodies

The measurement of relative motion between two moving bones is commonly accomplished for in vitro studies by attaching to each bone a series of either passive or active markers in a fixed orientation to create a rigid body (RB). This work determined the accuracy of motion between two RBs using an Optotrak optical motion capture system with active infrared LEDs. The stationary noise in the system was quantified by recording the apparent change in position with the RBs stationary and found to be 0.04 degrees and 0.03 mm. Incremental 10 degrees rotations and 10-mm translations were made using a more precise tool than the Optotrak. Increasing camera distance decreased the precision or increased the range of values observed for a set motion and increased the error in rotation or bias between the measured and actual rotation. The relative positions of the RBs with respect to the camera-viewing plane had a minimal effect on the kinematics and, therefore, for a given distance in the volume less than or close to the precalibrated camera distance, any motion was similarly reliable. For a typical operating set-up, a 10 degrees rotation showed a bias of 0.05 degrees and a 95% repeatability limit of 0.67 degrees. A 10-mm translation showed a bias of 0.03 mm and a 95% repeatability limit of 0.29 mm. To achieve a high level of accuracy it is important to keep the distance between the cameras and the markers near the distance the cameras are focused to during calibration.     

Measuring clinically relevant knee motion with a self-calibrated wearable sensor

Low-cost sensors provide a unique opportunity to continuously monitor patient progress during rehabilitation; however, these sensors have yet to demonstrate the fidelity and lack the calibration paradigms necessary to be viable tools for clinical research. The purpose of this study was to validate a low-cost wearable sensor that accurately measured peak knee extension during clinical exercises and needed no additional equipment for calibration. Sagittal plane knee motion was quantified using a 9-axis motion sensor and directly compared to motion capture data. The motion sensor measured the field strength of a strong earth magnet secured to the distal femur, which was correlated with knee angle during a simple calibration process. Peak knee motions and kinematic patterns were compared with motion capture data using paired t-tests and cross correlation, respectively. Peak extension values during seated knee extensions were accurate within 5 degrees across all subjects (root mean square error: 2.6 degrees, P = 0.29). Knee flexion during gait strongly correlated (0.84 ≤ r_xy ≤ 0.99) with motion capture measurements but demonstrated peak flexion errors of 10 degrees. In this study, we present a low-cost sensor (≈$ 35 US) that accurately determines knee extension angle following a calibration procedure that did not require any other equipment. Our findings demonstrate that this sensor paradigm is a feasible tool to monitor patient progress throughout physical therapy. However, dynamic motions that are associated with soft-tissue artifact may limit the accuracy of this type of wearable sensor.     

Comparison of measurement accuracy between two wrist goniometer systems during pronation and supination.

Pronation and supination have been shown to affect wrist goniometer measurement accuracy. The purpose of this study was to compare differences in measurement accuracy between a commonly used biaxial, single transducer wrist goniometer (System A) and a biaxial, two-transducer wrist goniometer (System B) over a wide range of pronation and supination (P/S) positions. Eight subjects moved their wrist between -40 and 40 degrees of flexion/extension (F/E) and -10 and 20 degrees of radial/ulnar (R/U) deviation in four different P/S positions: 90 degrees pronation; 45 degrees pronation; 0 degrees neutral and 45 degrees supination. System A was prone to more R/U crosstalk than System B and the amount of crosstalk was dependent on the P/S position. F/E crosstalk was present with both goniometer systems and was also shown to be dependent on P/S. When moving from pronation to supination, both systems experienced a similar extension offset error; however R/U offset errors were roughly equal in magnitude but opposite in direction. The calibration position will affect wrist angle measurements and the magnitude and direction of measurement errors. To minimize offset errors, the goniometer systems should be calibrated in the P/S posture most likely to be encountered during measurement. Differences in goniometer design and application accounted for the performance differences.     

Assessment of screw displacement axis accuracy and repeatability for joint kinematic description using an electromagnetic tracking device

Screw displacement axes (SDAs) have been employed to describe joint kinematics in biomechanical studies. Previous reports have investigated the accuracy of SDAs combining various motion analysis techniques and smoothing procedures. To our knowledge, no study has assessed SDA accuracy describing the relative movement between adjacent bodies with an electromagnetic tracking system. This is important, since in relative motion, neither body is fixed and consequently sensitivity to potential measurement errors from both bodies may be significant. Therefore, this study assessed the accuracy of SDAs for describing relative motion between two moving bodies. We analyzed numerical simulated data, and physical experimental data recorded using a precision jig and electromagnetic tracking device. The numerical simulations demonstrated SDA position accuracy (p=0.04) was superior for single compared to relative body motion, whereas orientation accuracy (p=0.2) was similar. Experimental data showed data-filtering (Butterworth filter) improved SDA position and orientation accuracies for rotation magnitudes smaller or equal to 5.0 degrees, with no effect at larger rotation magnitudes (p<0.05). This suggests that in absence of a filter, SDAs should only be calculated at rotations of greater than 5.0 degrees. For rotation magnitudes of 0.5 degrees (5.0 degrees ) about the SDA, SDA position and orientation error measurements determined from filtered experimental data were 3.75+/-0.30 mm (3.31+/-0.21 mm), and 1.10+/-0.04 degrees (1.04+/-0.03 degrees ), respectively. Experimental accuracy values describing the translation along and rotation about the SDA, were 0.06+/-0.00 mm and 0.09+/-0.01 degrees, respectively. These small errors establish the capability of SDAs to detect small translations, and rotations. In conclusion, application of SDAs should be a useful tool for describing relative motion in joint kinematic studies.     

Identification of key events in baseball hitting using inertial measurement units

Quantification of baseball hitting mechanics under game conditions help players to become successful batters and prevent injuries. Inertial measurement units (IMUs) can measure motion without any spatial restriction and are thus becoming a popular tool to investigate sports biomechanics. Biomechanical analysis of hitting requires the accurate detection of key events including “foot-off” while leaning back (F_Off), “foot-on” during forward swing (F_On), and ball impact. Ten male university baseball players hit a ball suspended on a T pole five times in kick-hitting and glide-hitting styles. Three IMUs were attached on mid-pelvis and on each hand to record acceleration and orientation data. The key events identified by the three IMUs were compared with those retrieved by an optical motion capture system with force platforms. The timings of the local peak acceleration of the pelvis in the direction of the pitcher that were recorded by the IMU closely matched those of F_Off and F_On events detected by the ground reaction force. Root mean square error (RMSE) between each measurement for the F_Off and F_On events were 0.024 and 0.031 s, respectively. The timing of the negative peak of acceleration in the proximal direction of the hands corresponded to the impact time determined by an optical motion capture system. RMSEs for the knob and barrel-side hand were 0.009 and 0.011 s, respectively. Our results demonstrate how IMUs can be useful for analyzing baseball hitting mechanics.     

Method for determining the spatial position of the shoulder with ultrasound-based motion analyzer.

Several methods have been developed recently for the analysis of the spatial motion of the scapula and the arm, whereby the spatial position of shoulder bones is determined in static conditions by interrupting motion. The authors have developed a 3D motion analysis method recording scapular motion in progress with appropriate accuracy in the course of arm movements of various degrees. The objective of this study is to explore the applicability of the method developed, as well as to compare it with and verify it by other methods developed earlier. The position and displacements of shoulder bones were determined on 30 shoulders of 15 healthy people. The newly developed measurement method is based on the mechanical basic principle stating that the position and motion of a rigid body -- in this case, the bones (segments) forming the shoulder joint -- can be calculated at any moment from the spatial coordinates of three points of a segment and any changes thereof in the course of motion. Ultrasound-based triplets providing the three points (fundamental points) by a segment as required for measurement were fixed on the sternum (modeling the trunk), the clavicle, the acromion (modeling the scapula), the upper arm, and the lower arm. The position of the sixteen anatomical points involved in the study were determined by an ultrasound-based pointer in the local coordinate system specified by the fundamental points before starting measurements. The ZEBRIS ultrasound-based motion analysis system was used for measuring the spatial coordinates of triplets in the course of continuous motion. The spatial coordinates of the designated anatomical points can be calculated by the method of triangulation. The method was calibrated by a ZEBRIS mapping (3DCAD) software commercially available, and the measurement error rate of the method was determined by statistical calculations. On the basis of calibration and error calculations it could be established that the accuracy and the reproducibility of the method were appropriate, in accordance with the limit values to be found in the literature.     

Accuracy map of an optical motion capture system with 42 or 21 cameras in a large measurement volume

Optical motion capture is commonly used in biomechanics to measure human kinematics. However, no studies have yet examined the accuracy of optical motion capture in a large capture volume (>100 m³), or how accuracy varies from the center to the extreme edges of the capture volume. This study measured the dynamic 3D errors of an optical motion capture system composed of 42 OptiTrack Prime 41 cameras (capture volume of 135 m³) by comparing the motion of a single marker to the motion reported by a ThorLabs linear motion stage. After spline interpolating the data, it was found that 97% of the capture area had error below 200 μm. When the same analysis was performed using only half (21) of the cameras, 91% of the capture area was below 200 μm of error. The only locations that exceeded this threshold were at the extreme edges of the capture area, and no location had a mean error exceeding 1 mm. When measuring human kinematics with skin-mounted markers, uncertainty of marker placement relative to underlying skeletal features and soft tissue artifact produce errors that are orders of magnitude larger than the errors attributed to the camera system itself. Therefore, the accuracy of this OptiTrack optical motion capture system was found to be more than sufficient for measuring full-body human kinematics with skin-mounted markers in a large capture volume (>100 m³).     

A novel validation and calibration method for motion capture systems based on micro-triangulation

Motion capture systems are widely used to measure human kinematics. Nevertheless, users must consider system errors when evaluating their results. Most validation techniques for these systems are based on relative distance and displacement measurements. In contrast, our study aimed to analyse the absolute volume accuracy of optical motion capture systems by means of engineering surveying reference measurement of the marker coordinates (uncertainty: 0.75 mm). The method is exemplified on an 18 camera OptiTrack Flex13 motion capture system. The absolute accuracy was defined by the root mean square error (RMSE) between the coordinates measured by the camera system and by engineering surveying (micro-triangulation). The original RMSE of 1.82 mm due to scaling error was managed to be reduced to 0.77 mm while the correlation of errors to their distance from the origin reduced from 0.855 to 0.209. A simply feasible but less accurate absolute accuracy compensation method using tape measure on large distances was also tested, which resulted in similar scaling compensation compared to the surveying method or direct wand size compensation by a high precision 3D scanner. The presented validation methods can be less precise in some respects as compared to previous techniques, but they address an error type, which has not been and cannot be studied with the previous validation methods.