scholarly journals Treadmill-to-Overground Mapping of Marker Trajectory for Treadmill-Based Continuous Gait Analysis

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 786
Author(s):  
Woo Chang Jung ◽  
Jung Keun Lee
Keyword(s):  
Gait Analysis ◽  
Mapping Method ◽  
Treadmill Walking ◽  
Measurement Unit ◽  
Position Vector ◽  
Motion Trajectories ◽  
Virtual Origin ◽  
Optical Motion ◽  
Walking Tests ◽  
Optical Motion Capture

A treadmill was used to perform continuous walking tests in a limited space that can be covered by marker-based optical motion capture systems. Most treadmill-based gait data are analyzed based on gait cycle percentage. However, achieving continuous walking motion trajectories over time without time normalization is often required, even if tests are performed under treadmill walking conditions. This study presents a treadmill-to-overground mapping method of optical marker trajectories for treadmill-based continuous gait analysis, by adopting a simple concept of virtual origin. The position vector from the backward moving virtual origin to a targeted marker within a limited walking volume is the same as the position vector from the fixed origin to the forward moving marker over the ground. With the proposed method, it is possible (i) to observe the change in physical quantity visually during the treadmill walking, and (ii) to obtain overground-mapped gait data for evaluating the accuracy of the inertial-measurement-unit-based trajectory estimation. The accuracy of the proposed method was verified from various treadmill walking tests, which showed that the total travel displacement error rate was 0.32% on average.

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.

scholarly journals Agreement of Gait Events Detection during Treadmill Backward Walking by Kinematic Data and Inertial Motion Units

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6331
Author(s):  
Uri Gottlieb ◽  
Tharani Balasukumaran ◽  
Jay R. Hoffman ◽  
Shmuel Springer
Keyword(s):  
Gait Analysis ◽  
Balance Control ◽  
Analysis Data ◽  
Inertial Motion ◽  
Unique Challenge ◽  
Backward Walking ◽  
Orthopedic Rehabilitation ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Events Detection

Backward walking (BW) is being increasingly used in neurologic and orthopedic rehabilitation as well as in sports to promote balance control as it provides a unique challenge to the sensorimotor control system. The identification of initial foot contact (IC) and terminal foot contact (TC) events is crucial for gait analysis. Data of optical motion capture (OMC) kinematics and inertial motion units (IMUs) are commonly used to detect gait events during forward walking (FW). However, the agreement between such methods during BW has not been investigated. In this study, the OMC kinematics and inertial data of 10 healthy young adults were recorded during BW and FW on a treadmill at different speeds. Gait events were measured using both kinematics and inertial data and then evaluated for agreement. Excellent reliability (Interclass Correlation > 0.9) was achieved for the identification of both IC and TC. The absolute differences between methods during BW were 18.5 ± 18.3 and 20.4 ± 15.2 ms for IC and TC, respectively, compared to 9.1 ± 9.6 and 10.0 ± 14.9 for IC and TC, respectively, during FW. The high levels of agreement between methods indicate that both may be used for some applications of BW gait analysis.

A gait analysis using optical motion capture system with large scale pressure sensor

2017 ◽  
Vol 2017 (0) ◽  
pp. A-36
Author(s):  
Tatsuro Ishizuka ◽  
Tokio Maeda ◽  
Sakura Yamaji ◽  
Yuji Ohgi ◽  
Humiaki Shibayama ◽  
...  
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pressure Sensor ◽  
Large Scale ◽  
Motion Capture System ◽  
Optical Motion ◽  
Optical Motion Capture

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.

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.

scholarly journals Using Accelerometer Data to Tune the Parameters of an Extended Kalman Filter for Optical Motion Capture: Preliminary Application to Gait Analysis

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 427
Author(s):  
Javier Cuadrado ◽  
Florian Michaud ◽  
Urbano Lugrís ◽  
Manuel Pérez Soto
Keyword(s):  
Kalman Filter ◽  
Gait Analysis ◽  
Extended Kalman Filter ◽  
Motion Capture ◽  
Reference Frames ◽  
Preliminary Test ◽  
Accelerometer Data ◽  
Capture Process ◽  
Optical Motion ◽  
Optical Motion Capture

Optical motion capture is currently the most popular method for acquiring motion data in biomechanical applications. However, it presents a number of problems that make the process difficult and inefficient, such as marker occlusions and unwanted reflections. In addition, the obtained trajectories must be numerically differentiated twice in time in order to get the accelerations. Since the trajectories are normally noisy, they need to be filtered first, and the selection of the optimal amount of filtering is not trivial. In this work, an extended Kalman filter (EKF) that manages marker occlusions and undesired reflections in a robust way is presented. A preliminary test with inertial measurement units (IMUs) is carried out to determine their local reference frames. Then, the gait analysis of a healthy subject is performed using optical markers and IMUs simultaneously. The filtering parameters used in the optical motion capture process are tuned in order to achieve good correlation between the obtained accelerations and those measured by the IMUs. The results show that the EKF provides a robust and efficient method for optical system-based motion analysis, and that the availability of accelerations measured by inertial sensors can be very helpful for the adjustment of the filters.

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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