Sensor-to-Segment Calibration Methodologies for Lower-Body Kinematic Analysis with Inertial Sensors: A Systematic Review

Kinematic analysis is indispensable to understanding and characterizing human locomotion. Thanks to the development of inertial sensors based on microelectronics systems, human kinematic analysis in an ecological environment is made possible. An important issue in human kinematic analyses with inertial sensors is the necessity of defining the orientation of the inertial sensor coordinate system relative to its underlying segment coordinate system, which is referred to sensor-to-segment calibration. Over the last decade, we have seen an increase of proposals for this purpose. The aim of this review is to highlight the different proposals made for lower-body segments. Three different databases were screened: PubMed, Science Direct and IEEE Xplore. One reviewer performed the selection of the different studies and data extraction. Fifty-five studies were included. Four different types of calibration method could be identified in the articles: the manual, static, functional, and anatomical methods. The mathematical approach to obtain the segment axis and the calibration evaluation were extracted from the selected articles. Given the number of propositions and the diversity of references used to evaluate the methods, it is difficult today to form a conclusion about the most suitable. To conclude, comparative studies are required to validate calibration methods in different circumstances.

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A Self-Calibration Method for Robotic Measurement System

10.1115/imece1999-0753 ◽

1999 ◽

Author(s):

Chunhe Gong ◽

Jingxia Yuan ◽

Jun Ni

Keyword(s):

Coordinate System ◽

Measurement System ◽

Calibration Method ◽

Position Measurement ◽

Robot Calibration ◽

Robot System ◽

Self Calibration ◽

World Coordinate System ◽

Robot Accuracy ◽

Calibration Methods

Abstract Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace.

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Estimation of 3D Knee Joint Angles during Cycling Using Inertial Sensors: Accuracy of a Novel Sensor-to-Segment Calibration Procedure Based on Pedaling Motion

Sensors ◽

10.3390/s19112474 ◽

2019 ◽

Vol 19 (11) ◽

pp. 2474 ◽

Cited By ~ 7

Author(s):

Sébastien Cordillet ◽

Nicolas Bideau ◽

Benoit Bideau ◽

Guillaume Nicolas

Keyword(s):

Knee Joint ◽

Inertial Sensor ◽

External Rotation ◽

Calibration Method ◽

Calibration Procedure ◽

Angle Measurement ◽

Measurement Unit ◽

Joint Angles ◽

Calibration Methods ◽

Flexion Extension

This paper presents a novel sensor-to-segment calibration procedure for inertial sensor-based knee joint kinematics analysis during cycling. This procedure was designed to be feasible in-field, autonomously, and without any external operator or device. It combines a static standing up posture and a pedaling task. The main goal of this study was to assess the accuracy of the new sensor-to-segment calibration method (denoted as the ‘cycling’ method) by calculating errors in terms of body-segment orientations and 3D knee joint angles using inertial measurement unit (IMU)-based and optoelectronic-based motion capture. To do so, 14 participants were evaluated during pedaling motion at a workload of 100 W, which enabled comparisons of the cycling method with conventional calibration methods commonly employed in gait analysis. The accuracy of the cycling method was comparable to that of other methods concerning the knee flexion/extension angle, and did not exceed 3.8°. However, the cycling method presented the smallest errors for knee internal/external rotation (6.65 ± 1.94°) and abduction/adduction (5.92 ± 2.85°). This study demonstrated that a calibration method based on the completion of a pedaling task combined with a standing posture significantly improved the accuracy of 3D knee joint angle measurement when applied to cycling analysis.

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Errors in micro-electro-mechanical systems inertial measurement and a review on present practices of error modelling

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217708237 ◽

2017 ◽

Vol 40 (9) ◽

pp. 2843-2854 ◽

Cited By ~ 4

Author(s):

Renu Bhardwaj ◽

Neelesh Kumar ◽

Vipan Kumar

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Mechanical Systems ◽

Stochastic Modelling ◽

Future Research ◽

Mems Devices ◽

Specific System ◽

Micro Electro Mechanical Systems ◽

Inertial Measurement ◽

Calibration Methods

Micro-electro-mechanical systems (MEMS) technology-based accelerometers and gyroscopes are small size, mass produced, low cost inertial sensors, which are now being used in aerospace, underwater vehicles, automotive, robotics, mobiles, gaming consoles, prosthetic devices and many other applications. MEMS inertial sensors are available in many grades in market and selecting the appropriate grade sensor is very important. Owing to interaction of different types of energies, different noises are generated in MEMS devices; these noises cause significant change in output and the first section of this paper illustrates that. In application, where MEMS inertial sensors are used, the accuracy, repeatability and reproducibility of inertia measurement is probed primarily by complex testing, using extensive range of physical stimuli. Noises in inertial measurement are generally dealt by designing a unit measurement model. Noises are treated as additive error in linear unit model and are modelled using various techniques so that errors can be compensated to improve the accuracy. This paper reviews the theory, framework and methodology used in the error model of a MEMS inertial sensor and stochastic modelling of measurement. Experimental results from the most commonly used Allan variance techniques are discussed. Error modelling methodology, consisting of testing and calibration methods, designing thermal model, stochastic modelling and parameter estimation techniques, is illustrated. Figures and tables under each section summarize features, merits, limitation and future research scope. This paper should serve as a single reference for researchers and engineers working on application specific system design and instrumentation using MEMS inertial sensors. Conclusion from the study should help in selecting the appropriate grade of sensor as well as the best error modelling as per the trade-off existing between accuracy and development cost of error modelling.

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A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units

Journal of Navigation ◽

10.1017/s0373463307004560 ◽

2008 ◽

Vol 61 (2) ◽

pp. 323-336 ◽

Cited By ~ 101

Author(s):

P. Aggarwal ◽

Z. Syed ◽

X. Niu ◽

N. El-Sheimy

Keyword(s):

Moving Objects ◽

Inertial Sensors ◽

Low Cost ◽

Calibration Method ◽

Calibration Procedure ◽

Measurement Unit ◽

Navigation Systems ◽

Integrated Navigation ◽

Mems Sensors ◽

Calibration Methods

Navigation involves the integration of methodologies and systems for estimating the time varying position and attitude of moving objects. Inertial Navigation Systems (INS) and the Global Positioning System (GPS) are among the most widely used navigation systems. The use of cost effective MEMS based inertial sensors has made GPS/INS integrated navigation systems more affordable. However MEMS sensors suffer from various errors that have to be calibrated and compensated to get acceptable navigation results. Moreover the performance characteristics of these sensors are highly dependent on the environmental conditions such as temperature variations. Hence there is a need for the development of accurate, reliable and efficient thermal models to reduce the effect of these errors that can potentially degrade the system performance. In this paper, the Allan variance method is used to characterize the noise in the MEMS sensors. A six-position calibration method is applied to estimate the deterministic sensor errors such as bias, scale factor, and non-orthogonality. An efficient thermal variation model is proposed and the effectiveness of the proposed calibration methods is investigated through a kinematic van test using integrated GPS and MEMS-based inertial measurement unit (IMU).

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A Self-Calibration Method for Robotic Measurement System

Journal of Manufacturing Science and Engineering ◽

10.1115/1.538916 ◽

1999 ◽

Vol 122 (1) ◽

pp. 174-181 ◽

Cited By ~ 22

Author(s):

Chunhe Gong ◽

Jingxia Yuan ◽

Jun Ni

Keyword(s):

Coordinate System ◽

Measurement System ◽

Calibration Method ◽

Position Measurement ◽

Robot Calibration ◽

Robot System ◽

Self Calibration ◽

World Coordinate System ◽

Robot Accuracy ◽

Calibration Methods

Robot calibration plays an increasingly important role in manufacturing. For robot calibration on the manufacturing floor, it is desirable that the calibration technique be easy and convenient to implement. This paper presents a new self-calibration method to calibrate and compensate for robot system kinematic errors. Compared with the traditional calibration methods, this calibration method has several unique features. First, it is not necessary to apply an external measurement system to measure the robot end-effector position for the purpose of kinematic identification since the robot measurement system has a sensor as its integral part. Second, this self-calibration is based on distance measurement rather than absolute position measurement for kinematic identification; therefore the calibration of the transformation from the world coordinate system to the robot base coordinate system, known as base calibration, is not necessary. These features not only greatly facilitate the robot system calibration, but also shorten the error propagation chain, therefore, increase the accuracy of parameter estimation. An integrated calibration system is designed to validate the effectiveness of this calibration method. Experimental results show that after calibration there is a significant improvement of robot accuracy over a typical robot workspace. [S1087-1357(00)01301-0]

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Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

Scientific Data ◽

10.1038/s41597-021-00995-8 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Ive Weygers ◽

Manon Kok ◽

Thomas Seel ◽

Darshan Shah ◽

Orçun Taylan ◽

...

Keyword(s):

Inertial Sensors ◽

Inertial Sensor ◽

Ground Truth ◽

Transformation Matrices ◽

True Model ◽

Advance Research ◽

Manual Palpation ◽

Visualization Tools ◽

Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

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A Multi Camera and Multi Laser Calibration Method for 3D Reconstruction of Revolution Parts

Sensors ◽

10.3390/s21030765 ◽

2021 ◽

Vol 21 (3) ◽

pp. 765

Author(s):

Hugo Álvarez ◽

Marcos Alonso ◽

Jairo R. Sánchez ◽

Alberto Izaguirre

Keyword(s):

3D Reconstruction ◽

Calibration Method ◽

Laser Plane ◽

Calibration Methods ◽

Laser Calibration ◽

Calibration Object ◽

Focus Plane ◽

Chessboard Pattern ◽

Line Patterns

This paper describes a method for calibrating multi camera and multi laser 3D triangulation systems, particularly for those using Scheimpflug adapters. Under this configuration, the focus plane of the camera is located at the laser plane, making it difficult to use traditional calibration methods, such as chessboard pattern-based strategies. Our method uses a conical calibration object whose intersections with the laser planes generate stepped line patterns that can be used to calculate the camera-laser homographies. The calibration object has been designed to calibrate scanners for revolving surfaces, but it can be easily extended to linear setups. The experiments carried out show that the proposed system has a precision of 0.1 mm.

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Smartphone-Based Inertial Odometry for Blind Walkers

Sensors ◽

10.3390/s21124033 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4033

Author(s):

Peng Ren ◽

Fatemeh Elyasi ◽

Roberto Manduchi

Keyword(s):

Particle Filtering ◽

Inertial Sensors ◽

State Of The Art ◽

Inertial Sensor ◽

Mean Shift ◽

Gait Pattern ◽

Comparative Assessment ◽

Pedestrian Tracking ◽

Step Counter ◽

Assisted Navigation

Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers.

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Stable Calibrations of Six-DOF Serial Robots by Using Identification Models with Equalized Singular Values

Robotica ◽

10.1017/s0263574721000229 ◽

2021 ◽

pp. 1-22

Author(s):

Zhouxiang Jiang ◽

Min Huang

Keyword(s):

Industrial Robot ◽

Singular Values ◽

Calibration Method ◽

Identification Accuracy ◽

Condition Numbers ◽

Measurement Instruments ◽

Unknown Parameters ◽

Calibration Methods ◽

Six Dof ◽

The Stability

SUMMARY In typical calibration methods (kinematic or non-kinematic) for serial industrial robot, though measurement instruments with high resolutions are adopted, measurement configurations are optimized, and redundant parameters are eliminated from identification model, calibration accuracy is still limited under measurement noise. This might be because huge gaps still exist among the singular values of typical identification Jacobians, thereby causing the identification models ill conditioned. This paper addresses such problem by using new identification models established in two steps. First, the typical models are divided into the submodels with truncated singular values. In this way, the unknown parameters corresponding to the abnormal singular values are removed, thereby reducing the condition numbers of the new submodels. However, these models might still be ill conditioned. Therefore, the second step is to further centralize the singular values of each submodel by using a matrix balance method. Afterward, all submodels are well conditioned and obtain much higher observability indices compared with those of typical models. Simulation results indicate that significant improvements in the stability of identification results and the identifiability of unknown parameters are acquired by using the new identification submodels. Experimental results indicate that the proposed calibration method increases the identification accuracy without incurring additional hardware setup costs to the typical calibration method.

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Vicarious Radiometric Calibration of Ocean Color Bands for FY-3D/MERSI-II at Lake Qinghai, China

Sensors ◽

10.3390/s21010139 ◽

2020 ◽

Vol 21 (1) ◽

pp. 139

Author(s):

Shengli Chen ◽

Xiaobing Zheng ◽

Xin Li ◽

Wei Wei ◽

Shenda Du ◽

...

Keyword(s):

Infrared Imaging ◽

Large Deviation ◽

Ocean Color ◽

Calibration Method ◽

Surface Measurement ◽

Radiometric Calibration ◽

Lake Qinghai ◽

Vicarious Calibration ◽

Calibration Methods ◽

Spectral Imager

To calibrate the low signal response of the ocean color (OC) bands and test the stability of the Fengyun-3D (FY-3D)/Medium Resolution Spectral Imager II (MERSI-II), an absolute radiometric calibration field test of FY-3D/MERSI-II at the Lake Qinghai Radiometric Calibration Site (RCS) was carried out in August 2018. The lake surface and atmospheric parameters were mainly measured by advanced observation instruments, and the MODerate spectral resolution atmospheric TRANsmittance algorithm and computer model (MODTRAN4.0) was used to simulate the multiple scattering radiance value at the altitude of the sensor. The results showed that the relative deviations between bands 9 and 12 are within 5.0%, while the relative deviations of bands 8, and 13 are 17.1%, and 12.0%, respectively. The precision of the calibration method was verified by calibrating the Aqua/Moderate-resolution Imaging Spectroradiometer (MODIS) and National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer (VIIRS), and the deviation of the calibration results was evaluated with the results of the Dunhuang RCS calibration and lunar calibration. The results showed that the relative deviations of NPP/VIIRS were within 7.0%, and the relative deviations of Aqua/MODIS were within 4.1% from 400 nm to 600 nm. The comparisons of three on-orbit calibration methods indicated that band 8 exhibited a large attenuation after launch and the calibration results had good consistency at the other bands except for band 13. The uncertainty value of the whole calibration system was approximately 6.3%, and the uncertainty brought by the field surface measurement reached 5.4%, which might be the main reason for the relatively large deviation of band 13. This study verifies the feasibility of the vicarious calibration method at the Lake Qinghai RCS and provides the basis and reference for the subsequent on-orbit calibration of FY-3D/MERSI-II.

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