scholarly journals Validation of Thigh Angle Estimation Using Inertial Measurement Unit Data against Optical Motion Capture Systems

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 596 ◽  
Author(s):  
Nimsiri Abhayasinghe ◽  
Iain Murray ◽  
Shiva Sharif Bidabadi
Keyword(s):  
Motion Capture ◽  
Computation Time ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Orientation Estimation ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Inertial measurement units are commonly used to estimate the orientation of sections of sections of human body in inertial navigation systems. Most of the algorithms used for orientation estimation are computationally expensive and it is difficult to implement them in real-time embedded systems with restricted capabilities. This paper discusses a computationally inexpensive orientation estimation algorithm (Gyro Integration-Based Orientation Filter—GIOF) that is used to estimate the forward and backward swing angle of the thigh (thigh angle) for a vision impaired navigation aid. The algorithm fuses the accelerometer and gyroscope readings to derive the single dimension orientation in such a way that the orientation is corrected using the accelerometer reading when it reads gravity only or otherwise integrate the gyro reading to estimate the orientation. This strategy was used to reduce the drift caused by the gyro integration. The thigh angle estimated by GIOF was compared against the Vicon Optical Motion Capture System and reported a mean correlation of 99.58% for 374 walking trials with a standard deviation of 0.34%. The Root Mean Square Error (RMSE) of the thigh angle estimated by GIOF compared with Vicon measurement was 1.8477°. The computation time on an 8-bit microcontroller running at 8 MHz for GIOF is about a half of that of Complementary Filter implementation. Although GIOF was only implemented and tested for estimating pitch of the IMU, it can be easily extended into 2D to estimate both pitch and roll.

scholarly journals Measuring Spinal Mobility Using an Inertial Measurement Unit System: A Validation Study in Axial Spondyloarthritis

Diagnostics ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 426
Author(s):  
I. Concepción Aranda-Valera ◽  
Antonio Cuesta-Vargas ◽  
Juan L. Garrido-Castro ◽  
Philip V. Gardiner ◽  
Clementina López-Medina ◽  
...  
Keyword(s):  
Outcome Measures ◽  
Motion Capture ◽  
Axial Spondyloarthritis ◽  
Human Motion ◽  
Spinal Mobility ◽  
Measurement Unit ◽  
Human Motion Analysis ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Optical Motion Capture

Portable inertial measurement units (IMUs) are beginning to be used in human motion analysis. These devices can be useful for the evaluation of spinal mobility in individuals with axial spondyloarthritis (axSpA). The objectives of this study were to assess (a) concurrent criterion validity in individuals with axSpA by comparing spinal mobility measured by an IMU sensor-based system vs. optical motion capture as the reference standard; (b) discriminant validity comparing mobility with healthy volunteers; (c) construct validity by comparing mobility results with relevant outcome measures. A total of 70 participants with axSpA and 20 healthy controls were included. Individuals with axSpA completed function and activity questionnaires, and their mobility was measured using conventional metrology for axSpA, an optical motion capture system, and an IMU sensor-based system. The UCOASMI, a metrology index based on measures obtained by motion capture, and the IUCOASMI, the same index using IMU measures, were also calculated. Descriptive and inferential analyses were conducted to show the relationships between outcome measures. There was excellent agreement (ICC > 0.90) between both systems and a significant correlation between the IUCOASMI and conventional metrology (r = 0.91), activity (r = 0.40), function (r = 0.62), quality of life (r = 0.55) and structural change (r = 0.76). This study demonstrates the validity of an IMU system to evaluate spinal mobility in axSpA. These systems are more feasible than optical motion capture systems, and they could be useful in clinical practice.

Riding Motion Capture System Using Inertial Measurement Units with Contact Constraints

2019 ◽  
Vol 13 (4) ◽  
pp. 506-516 ◽  
Author(s):  
Tsubasa Maruyama ◽  
Mitsunori Tada ◽  
Haruki Toda ◽  
◽  
Keyword(s):  
Motion Capture ◽  
Bicycle Ergometer ◽  
Human Motion ◽  
Position Estimation ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Outdoor Environments ◽  
Optical Motion ◽  
Optical Motion Capture

The measurement of human motion is an important aspect of ergonomic mobility design, in which the mobility product is evaluated based on human factors obtained by digital human (DH) technologies. The optical motion-capture (MoCap) system has been widely used for measuring human motion in laboratories. However, it is generally difficult to measure human motion using mobility products in real-world scenarios, e.g., riding a bicycle on an outdoor slope, owing to unstable lighting conditions and camera arrangements. On the other hand, the inertial-measurement-unit (IMU)-based MoCap system does not require any optical devices, providing the potential for measuring riding motion even in outdoor environments. However, in general, the estimated motion is not necessarily accurate as there are many errors due to the nature of the IMU itself, such as drift and calibration errors. Thus, it is infeasible to apply the IMU-based system to riding motion estimation. In this study, we develop a new riding MoCap system using IMUs. The proposed system estimates product and human riding motions by combining the IMU orientation with contact constraints between the product and DH, e.g., DH hands in contact with handles. The proposed system is demonstrated with a bicycle ergometer, including the handles, seat, backrest, and foot pedals, as in general mobility products. The proposed system is further validated by comparing the estimated joint angles and positions with those of the optical MoCap for three different subjects. The experiment reveals both the effectiveness and limitations of the proposed system. It is confirmed that the proposed system improves the joint position estimation accuracy compared with a system using only IMUs. The angle estimation accuracy is also improved for near joints. However, it is observed that the angle accuracy decreases for a few joints. This is explained by the fact that the proposed system modifies the orientations of all body segments to satisfy the contact constraints, even if the orientations of a few joints are correct. This further confirms that the elapsed time using the proposed system is sufficient for real-time application.

scholarly journals Proposed Mobility Assessments with Simultaneous Full-Body Inertial Measurement Units and Optical Motion Capture in Healthy Adults and Neurological Patients for Future Validation Studies: Study Protocol

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5833
Author(s):  
Elke Warmerdam ◽  
Robbin Romijnders ◽  
Johanna Geritz ◽  
Morad Elshehabi ◽  
Corina Maetzler ◽  
...  
Keyword(s):  
Motion Capture ◽  
Healthy Adults ◽  
Response To Treatment ◽  
Inertial Measurement Units ◽  
Mobility Patterns ◽  
Inertial Measurement ◽  
Neurological Patients ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Healthy adults and neurological patients show unique mobility patterns over the course of their lifespan and disease. Quantifying these mobility patterns could support diagnosing, tracking disease progression and measuring response to treatment. This quantification can be done with wearable technology, such as inertial measurement units (IMUs). Before IMUs can be used to quantify mobility, algorithms need to be developed and validated with age and disease-specific datasets. This study proposes a protocol for a dataset that can be used to develop and validate IMU-based mobility algorithms for healthy adults (18–60 years), healthy older adults (>60 years), and patients with Parkinson’s disease, multiple sclerosis, a symptomatic stroke and chronic low back pain. All participants will be measured simultaneously with IMUs and a 3D optical motion capture system while performing standardized mobility tasks and non-standardized activities of daily living. Specific clinical scales and questionnaires will be collected. This study aims at building the largest dataset for the development and validation of IMU-based mobility algorithms for healthy adults and neurological patients. It is anticipated to provide this dataset for further research use and collaboration, with the ultimate goal to bring IMU-based mobility algorithms as quickly as possible into clinical trials and clinical routine.

scholarly journals IMU gyroscopes are a valid alternative to 3D optical motion capture system for angular kinematics analysis in tennis

2020 ◽  
pp. 175433712096544 ◽  
Author(s):  
Gabriel Delgado-García ◽  
Jos Vanrenterghem ◽  
Emilio J Ruiz-Malagón ◽  
Pablo Molina-García ◽  
Javier Courel-Ibáñez ◽  
...  
Keyword(s):  
Angular Velocity ◽  
Motion Capture ◽  
Correlation Coefficients ◽  
Strong Relationship ◽  
System Level ◽  
Optical Motion ◽  
Measurement Units ◽  
The Difference ◽  
Optical Motion Capture ◽  
Level Of Agreement

Whereas 3D optical motion capture (OMC) systems are considered the gold standard for kinematic assessment in sport science, they present some drawbacks that limit its use in the field. Inertial measurement units (IMUs) incorporating gyroscopes have been considered as a more practical alternative. Thus, the aim of the study was to evaluate the level of agreement for angular velocity between IMU gyroscopes and an OMC system for varying tennis strokes and intensities. In total, 240 signals of angular velocity from different body segments and types of strokes (forehand, backhand and service) were recorded from four players (two competition players and two beginners). The angular velocity of the IMU gyroscopes was compared to the angular velocity from the OMC system. Level of agreement was evaluated by correlation coefficients, magnitudes of errors in absolute and relative values and Bland-Altman plots. Differences between both systems were highly consistent within players’ skill (i.e. along the broad range of velocities) and axes ( x, y, z). Correlations ranged from 0.951 to 0.993, indicating a very strong relationship and concordance. The magnitude of the differences ranged from 4.4 to 35.4 deg·s−1. The difference relative to the maximum angular velocity achieved was less than 5.0%. The study concluded that IMUs and OMC systems showed comparable values. Thus, IMUs seem to be a valid alternative to detect meaningful differences in angular velocity during tennis groundstrokes in field-based experimentation.

scholarly journals In Vivo Measurement of Wrist Movements during the Dart-Throwing Motion Using Inertial Measurement Units

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5623
Author(s):  
Gabriella Fischer ◽  
Michael Alexander Wirth ◽  
Simone Balocco ◽  
Maurizio Calcagni
Keyword(s):  
Range Of Motion ◽  
Motion Capture ◽  
Absolute Difference ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Mean Absolute Difference ◽  
Inertial Measurement ◽  
Dart Throwing ◽  
Measurement Units ◽  
Throwing Motion

Background: This study investigates the dart-throwing motion (DTM) by comparing an inertial measurement unit-based system previously validated for basic motion tasks with an optoelectronic motion capture system. The DTM is interesting as wrist movement during many activities of daily living occur in this movement plane, but the complex movement is difficult to assess clinically. Methods: Ten healthy subjects were recorded while performing the DTM with their right wrist using inertial sensors and skin markers. Maximum range of motion obtained by the different systems and the mean absolute difference were calculated. Results: In the flexion–extension plane, both systems calculated a range of motion of 100° with mean absolute differences of 8°, while in the radial–ulnar deviation plane, a mean absolute difference of 17° and range of motion values of 48° for the optoelectronic system and 59° for the inertial measurement units were found. Conclusions: This study shows the challenge of comparing results of different kinematic motion capture systems for complex movements while also highlighting inertial measurement units as promising for future clinical application in dynamic and coupled wrist movements. Possible sources of error and solutions are discussed.

scholarly journals Comparison of Trotting Stance Detection Methods from an Inertial Measurement Unit Mounted on the Horse’s Limb

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2983
Author(s):  
Marie Sapone ◽  
Pauline Martin ◽  
Khalil Ben Mansour ◽  
Henry Château ◽  
Frédéric Marin
Keyword(s):  
Motion Capture ◽  
Inertial Measurement Unit ◽  
Detection Methods ◽  
Measurement Unit ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
New Methods ◽  
Measurement Units ◽  
Ground Types ◽  
Stride Duration

The development of on-board sensors, such as inertial measurement units (IMU), has made it possible to develop new methods for analyzing horse locomotion to detect lameness. The detection of spatiotemporal events is one of the keystones in the analysis of horse locomotion. This study assesses the performance of four methods for detecting Foot on and Foot off events. They were developed from an IMU positioned on the canon bone of eight horses during trotting recording on a treadmill and compared to a standard gold method based on motion capture. These methods are based on accelerometer and gyroscope data and use either thresholding or wavelets to detect stride events. The two methods developed from gyroscopic data showed more precision than those developed from accelerometric data with a bias less than 0.6% of stride duration for Foot on and 0.1% of stride duration for Foot off. The gyroscope is less impacted by the different patterns of strides, specific to each horse. To conclude, methods using the gyroscope present the potential of further developments to investigate the effects of different gait paces and ground types in the analysis of horse locomotion.

scholarly journals OpenSense: An open-source toolbox for Inertial-Measurement-Unit-based measurement of lower extremity kinematics over long durations

2021 ◽  
Author(s):  
Mazen Al Borno ◽  
Johanna O'Day ◽  
Vanessa Ibarra ◽  
James Dunne ◽  
Ajay Seth ◽  
...  
Keyword(s):  
Lower Extremity ◽  
Open Source ◽  
Motion Capture ◽  
Joint Kinematics ◽  
Natural Environments ◽  
Joint Angles ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Over Time

Background: The ability to measure joint kinematics in natural environments over long durations using inertial measurement units (IMUs) could enable at-home monitoring and personalized treatment of neurological and musculoskeletal disorders. However, drift, or the accumulation of error over time, inhibits the accurate measurement of movement over long durations. We sought to develop an open-source workflow to estimate lower extremity joint kinematics from IMU data that was accurate, and capable of assessing and mitigating drift. Methods: We computed IMU-based estimates of kinematics using sensor fusion and an inverse kinematics approach with a constrained biomechanical model. We measured kinematics for 11 subjects as they performed two 10-minute trials: walking and a repeated sequence of varied lower-extremity movements. To validate the approach, we compared the joint angles computed with IMU orientations to the joint angles computed from optical motion capture using root mean square (RMS) difference and Pearson correlations, and estimated drift using a linear regression on each subject's RMS differences over time. Results: IMU-based kinematic estimates agreed with optical motion capture; median RMS differences over all subjects and all minutes were between 3-6 degrees for all joint angles except hip rotation and correlation coefficients were moderate to strong (r = 0.60 to 0.87). We observed minimal drift in the RMS differences over ten minutes; the average slopes of the linear fits to these data were near zero (-0.14 to 0.17 deg/min). Conclusions: Our workflow produced joint kinematics consistent with those estimated by optical motion capture, and could mitigate kinematic drift even in the trials of continuous walking without rest, obviating the need for explicit sensor recalibration (e.g. sitting or standing still for a few seconds or zero-velocity updates) used in current drift-mitigation approaches. This could enable long-duration measurements, bringing the field one step closer to estimating kinematics in natural environments.

Dynamic Joint Motions in Occupational Environments as Indicators of Potential Musculoskeletal Injury Risk

2020 ◽  
pp. 1-8
Author(s):  
Jonathan S. Dufour ◽  
Alexander M. Aurand ◽  
Eric B. Weston ◽  
Christopher N. Haritos ◽  
Reid A. Souchereau ◽  
...  
Keyword(s):  
Motion Capture ◽  
Task Difficulty ◽  
Injury Risk ◽  
Occupational Injury ◽  
Measurement Unit ◽  
Motion Capture System ◽  
Motion Data ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Joint Motions

The objective of this study was to test the feasibility of using a pair of wearable inertial measurement unit (IMU) sensors to accurately capture dynamic joint motion data during simulated occupational conditions. Eleven subjects (5 males and 6 females) performed repetitive neck, low-back, and shoulder motions simulating low- and high-difficulty occupational tasks in a laboratory setting. Kinematics for each of the 3 joints were measured via IMU sensors in addition to a “gold standard” passive marker optical motion capture system. The IMU accuracy was benchmarked relative to the optical motion capture system, and IMU sensitivity to low- and high-difficulty tasks was evaluated. The accuracy of the IMU sensors was found to be very good on average, but significant positional drift was observed in some trials. In addition, IMU measurements were shown to be sensitive to differences in task difficulty in all 3 joints (P < .05). These results demonstrate the feasibility for using wearable IMU sensors to capture kinematic exposures as potential indicators of occupational injury risk. Velocities and accelerations demonstrate the most potential for developing risk metrics since they are sensitive to task difficulty and less sensitive to drift than rotational position measurements.

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