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

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 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 Comparison of Gait Analysis Between a Triaxial Accelerometer-Based Device and an Optical Motion Capture System

2020 ◽  
Author(s):  
Taisuke Ito ◽  
Yuichi Ota
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Measurement Accuracy ◽  
Reaction Force ◽  
Correlation Coefficients ◽  
P Value ◽  
Motion Capture System ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Good Agreement

AYUMI EYE is an accelerometer-based gait analysis device that measures the 3D accelerations of the human trunk. This study investigated the measurement accuracy of the AYUMI EYE as hardware as well as the accuracy of the gait cycle extraction program via simultaneous measurements using AYUMI EYE, a ground reaction force (GRF), and an optical motion capture system called VICON. The study was conducted with four healthy individuals as participants. The gait data were obtained by simulating four different patterns for three trials each: normal walking, anterior-tilt walking, hemiplegic walking, and shuffling walking. The AYUMI EYE and VICON showed good agreement for both the acceleration and displacement data. The durations of subsequent stride cycles calculated using the AYUMI EYE and GRF were in good agreement based on the calculated cross-correlation coefficients (CCs) with an r value of 0.896 and p-value less than 0.05, and their accuracies for these results were sufficient.

Tracking of Markers for 2D and 3D Gait Analysis Using Home Video Cameras

2013 ◽  
Vol 4 (3) ◽  
pp. 36-52 ◽  
Author(s):  
Sandro Mihradi ◽  
Ferryanto ◽  
Tatacipta Dirgantara ◽  
Andi I. Mahyuddin
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Motion Capture System ◽  
Distance Method ◽  
Home Video ◽  
Video Cameras ◽  
Optical Motion ◽  
Spatio Temporal ◽  
Optical Motion Capture ◽  
2D And 3D

This work presents the development of an affordable optical motion-capture system which uses home video cameras for 2D and 3D gait analysis. The 2D gait analyzer system consists of one camcorder and one PC while the 3D gait analyzer system uses two camcorders, a flash and two PCs. Both systems make use of 25 fps camcorder, LED markers and technical computing software to track motions of markers attached to human body during walking. In the experiment for 3D gait analyzer system, the two cameras are synchronized by using flash. The recorded videos for both systems are extracted into frames and then converted into binary images, and bridge morphological operation is applied for unconnected pixel to facilitate marker detection process. Least distance method is then employed to track the markers motions, and 3D Direct Linear Transformation is used to reconstruct 3D markers positions. The correlation between length in pixel and in the real world resulted from calibration process is used to reconstruct 2D markers positions. To evaluate the reliability of the 2D and 3D optical motion-capture system developed in the present work, spatio-temporal and kinematics parameters calculated from the obtained markers positions are qualitatively compared with the ones from literature, and the results show good compatibility.

scholarly journals Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 789
Author(s):  
David Kreuzer ◽  
Michael Munz
Keyword(s):  
Gait Analysis ◽  
Time Series Data ◽  
Low Cost ◽  
Analysis Data ◽  
Gait Disorders ◽  
Human Gait ◽  
Series Data ◽  
Ageing Society ◽  
Time Motion ◽  
Gait Phase

With an ageing society comes the increased prevalence of gait disorders. The restriction of mobility leads to a considerable reduction in the quality of life, because associated falls increase morbidity and mortality. Consideration of gait analysis data often alters surgical recommendations. For that reason, the early and systematic diagnostic treatment of gait disorders can spare a lot of suffering. As modern gait analysis systems are, in most cases, still very costly, many patients are not privileged enough to have access to comparable therapies. Low-cost systems such as inertial measurement units (IMUs) still pose major challenges, but offer possibilities for automatic real-time motion analysis. In this paper, we present a new approach to reliably detect human gait phases, using IMUs and machine learning methods. This approach should form the foundation of a new medical device to be used for gait analysis. A model is presented combining deep 2D-convolutional and LSTM networks to perform a classification task; it predicts the current gait phase with an accuracy of over 92% on an unseen subject, differentiating between five different phases. In the course of the paper, different approaches to optimize the performance of the model are presented and evaluated.

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