scholarly journals Validation of 3-Space Wireless Inertial Measurement Units Using an Industrial Robot

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6858
Author(s):  
Jaime Hislop ◽  
Mats Isaksson ◽  
John McCormick ◽  
Chris Hensman
Keyword(s):  
Motion Tracking ◽  
Industrial Robot ◽  
Inertial Measurement Units ◽  
Electromagnetic Noise ◽  
Inertial Measurement ◽  
Sensor Orientation ◽  
Fusion Methods ◽  
Optical Motion ◽  
Measurement Units ◽  
Optical Motion Capture

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.

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.

Evaluation of inertial measurement units as a novel method for kinematic gait evaluation in dogs

2016 ◽  
Vol 29 (06) ◽  
pp. 475-483 ◽  
Author(s):  
Alexandra Pauls ◽  
Chris Kawcak ◽  
Kevin Haussler ◽  
Gina Bertocci ◽  
Valerie Moorman ◽  
...  
Keyword(s):  
Data Acquisition ◽  
Sagittal Plane ◽  
Plane Motion ◽  
Inertial Measurement Units ◽  
Hip Joints ◽  
Kinematic Data ◽  
Inertial Measurement ◽  
Optical Motion ◽  
Measurement Units ◽  
Plane Joint

Summary Objective: To evaluate the use of inertial measurement units (IMU) for quantification of canine limb kinematics. Methods: Sixteen clinically healthy, medium-sized dogs were enrolled. Baseline kinematic data were acquired using an optical motion capture system. Following this baseline data acquisition, a harness system was used for attachment of IMU to the animals. Optical kinematic data of dogs with and without the harness were compared to evaluate the influence of the harness on gait parameters. Sagittal plane joint kinematics acquired simultaneously with IMU and the optical system were compared for the carpal, tarsal, stifle and hip joints. Comparisons of data were made using the concordance correlation coefficient (CCC) test and evaluation of root mean squared errors (RMSE). Results: No significant differences were demonstrated in stance duration, swing duration or stride length between dogs instrumented with or without the harness, however, mean RMSE values ranged from 4.90° to 14.10° across the various joints. When comparing simultaneously acquired optical and IMU kinematic data, strong correlations were found for all four joints evaluated (CCC: carpus = 0.98, hock = 0.95, stifle = 0.98, hip = 0.96) and median RMSE values were similar across the joints ranging from 2.51° to 3.52°. Conclusions and Clinical relevance: Canine sagittal plane motion data acquisition with IMU is feasible, and optically acquired and IMU acquired sagittal plane kinematics had good correlation. This technology allows data acquisition outside the gait laboratory and may provide an alternative to optical kinematic gait analysis for the carpal, tarsal, stifle, and hip joints in the dog. Further investigation into this technology is indicated.

scholarly journals Metrological Evaluation of Human–Robot Collaborative Environments Based on Optical Motion Capture Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3748
Author(s):  
Leticia González ◽  
Juan C. Álvarez ◽  
Antonio M. López ◽  
Diego Álvarez
Keyword(s):  
Motion Capture ◽  
Motion Tracking ◽  
Industrial Robot ◽  
Human Motion ◽  
Factors Affecting ◽  
Collaborative Environment ◽  
Accuracy And Precision ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Metrological Evaluation

In the context of human–robot collaborative shared environments, there has been an increase in the use of optical motion capture (OMC) systems for human motion tracking. The accuracy and precision of OMC technology need to be assessed in order to ensure safe human–robot interactions, but the accuracy specifications provided by manufacturers are easily influenced by various factors affecting the measurements. This article describes a new methodology for the metrological evaluation of a human–robot collaborative environment based on optical motion capture (OMC) systems. Inspired by the ASTM E3064 test guide, and taking advantage of an existing industrial robot in the production cell, the system is evaluated for mean error, error spread, and repeatability. A detailed statistical study of the error distribution across the capture area is carried out, supported by a Mann–Whitney U-test for median comparisons. Based on the results, optimal capture areas for the use of the capture system are suggested. The results of the proposed method show that the metrological characteristics obtained are compatible and comparable in quality to other methods that do not require the intervention of an industrial robot.

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.

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