scholarly journals Concurrent validity of human pose tracking in video for measuring gait parameters in older adults: a preliminary analysis with multiple trackers, viewing angles, and walking directions

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Sina Mehdizadeh ◽  
Hoda Nabavi ◽  
Andrea Sabo ◽  
Twinkle Arora ◽  
Andrea Iaboni ◽  
...  
Keyword(s):  
Older Adults ◽  
Motion Capture ◽  
Automated Analysis ◽  
Video Data ◽  
Human Gait ◽  
Motion Capture System ◽  
Pose Tracking ◽  
Tracking Algorithms ◽  
Gait Parameters ◽  
Monitoring Technologies

Abstract Background Many of the available gait monitoring technologies are expensive, require specialized expertise, are time consuming to use, and are not widely available for clinical use. The advent of video-based pose tracking provides an opportunity for inexpensive automated analysis of human walking in older adults using video cameras. However, there is a need to validate gait parameters calculated by these algorithms against gold standard methods for measuring human gait data in this population. Methods We compared quantitative gait variables of 11 older adults (mean age = 85.2) calculated from video recordings using three pose trackers (AlphaPose, OpenPose, Detectron) to those calculated from a 3D motion capture system. We performed comparisons for videos captured by two cameras at two different viewing angles, and viewed from the front or back. We also analyzed the data when including gait variables of individual steps of each participant or each participant’s averaged gait variables. Results Our findings revealed that, i) temporal (cadence and step time), but not spatial and variability gait measures (step width, estimated margin of stability, coefficient of variation of step time and width), calculated from the video pose tracking algorithms correlate significantly to that of motion capture system, and ii) there are minimal differences between the two camera heights, and walks viewed from the front or back in terms of correlation of gait variables, and iii) gait variables extracted from AlphaPose and Detectron had the highest agreement while OpenPose had the lowest agreement. Conclusions There are important opportunities to evaluate models capable of 3D pose estimation in video data, improve the training of pose-tracking algorithms for older adult and clinical populations, and develop video-based 3D pose trackers specifically optimized for quantitative gait measurement.

scholarly journals Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

2021 ◽  
pp. 110414
Author(s):  
Robert M. Kanko ◽  
Elise K. Laende ◽  
Gerda Strutzenberger ◽  
Marcus Brown ◽  
W. Scott Selbie ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Capture ◽  
Learning Algorithm ◽  
Motion Capture System ◽  
Deep Learning Algorithm ◽  
Markerless Motion Capture ◽  
Gait Parameters

scholarly journals Human gait assessment using a 3D marker-less multimodal motion capture system

2019 ◽  
Vol 79 (3-4) ◽  
pp. 2629-2651 ◽  
Author(s):  
Thiago Braga Rodrigues ◽  
Debora Pereira Salgado ◽  
Ciarán Ó Catháin ◽  
Noel O’Connor ◽  
Niall Murray
Keyword(s):  
Motion Capture ◽  
Human Gait ◽  
Motion Capture System ◽  
Gait Assessment

scholarly journals Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Andrea Sabo ◽  
Sina Mehdizadeh ◽  
Kimberley-Dale Ng ◽  
Andrea Iaboni ◽  
Babak Taati
Keyword(s):  
Older Adults ◽  
Video Data ◽  
Pose Tracking ◽  
Human Pose Tracking ◽  
Human Pose

DEVELOPMENT OF MOTION CAPTURE SYSTEM USING DUAL VIDEO CAMERAS FOR THE GAIT DESIGN OF A BIPED ROBOT

2011 ◽  
Vol 08 (02) ◽  
pp. 275-299 ◽  
Author(s):  
JUNG-YUP KIM ◽  
YOUNG-SEOG KIM
Keyword(s):  
Motion Capture ◽  
Human Walking ◽  
Human Gait ◽  
Motion Capture System ◽  
System Calibration ◽  
Walking Gait ◽  
Video Cameras ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Motion Capture Systems

This paper, describes the development of a motion capture system with novel features for biped robots. In general, motion capture is effectively utilized in the field of computer animation. In the field of humanoid robotics, the number of studies attempting to design human-like gaits by using expensive optical motion capture systems is increasing. The optical motion capture systems used in these studies have involved a large number of cameras because such systems use small-sized ball markers; hence the position accuracy of the markers and the system calibration are very significant. However, since the human walking gait is a simple periodic motion rather than a complex motion, we have developed a specialized motion capture system for this study using dual video cameras and large band-type markers without high-level system calibration in order to capture the human walking gait. In addition to its lower complexity, the proposed capture method requires only a low-cost system and has high space efficiency. An image processing algorithm is also proposed for deriving the human gait data. Finally, we verify the reliability and accuracy of our system by comparing a zero moment point (ZMP) trajectory calculated by the motion captured data with a ZMP trajectory measured by foot force sensors.

scholarly journals Assessment of spatiotemporal gait parameters using a deep learning algorithm-based markerless motion capture system

2020 ◽  
Author(s):  
Robert Kanko ◽  
Gerda Strutzenberger ◽  
Marcus Brown ◽  
Scott Selbie ◽  
Kevin Deluzio
Keyword(s):  
Motion Capture ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Healthy Adults ◽  
Video Data ◽  
Heel Strike ◽  
Markerless Motion Capture ◽  
Gait Parameters

Spatiotemporal parameters can characterize the gait patterns of individuals, allowing assessment of their health status and detection of clinically meaningful changes in their gait. Video-based markerless motion capture is a user-friendly, inexpensive, and widely applicable technology that could reduce the barriers to measuring spatiotemporal gait parameters in clinical and more diverse settings. Two studies were performed to determine whether gait parameters measured using markerless motion capture demonstrate concurrent validity with those measured using marker-based motion capture and pressure sensitive gait mats. For the first study, thirty healthy adults performed treadmill gait at self-selected speeds while marker-based motion capture and synchronized video data were recorded simultaneously. For the second study, twenty-five healthy adults performed over-ground gait at self-selected speeds while footfalls were recorded using a gait mat and synchronized video data were recorded simultaneously. Kinematic heel-strike and toe-off gait events were used to identify the same gait cycles between systems. Nine spatiotemporal gait parameters were measured by each system and directly compared between systems. Measurements were compared using Bland-Altman methods, mean differences, Pearson correlation coefficients, and intraclass correlation coefficients. The results indicate that markerless measurements of spatiotemporal gait parameters have good to excellent agreement with marker-based motion capture and gait mat systems, except for stance time and double limb support time relative to both systems and stride width relative to the gait mat. These findings indicate that markerless motion capture can adequately measure spatiotemporal gait parameters during treadmill and overground gait.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2020 ◽  
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Gold Standard ◽  
Three Dimensional ◽  
Human Gait ◽  
Large Set ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

ABSTRACTWalking is the primary mode of human locomotion. Accordingly, people have been interested in studying human gait since at least the fourth century BC. Human gait analysis is now common in many fields of clinical and basic research, but gold standard approaches – e.g., three-dimensional motion capture, instrumented mats or footwear, and wearables – are often expensive, immobile, data-limited, and/or require specialized equipment or expertise for operation. Recent advances in video-based pose estimation have suggested exciting potential for analyzing human gait using only two-dimensional video inputs collected from readily accessible devices (e.g., smartphones, tablets). However, we currently lack: 1) data about the accuracy of video-based pose estimation approaches for human gait analysis relative to gold standard measurement techniques and 2) an available workflow for performing human gait analysis via video-based pose estimation. In this study, we compared a large set of spatiotemporal and sagittal kinematic gait parameters as measured by OpenPose (a freely available algorithm for video-based human pose estimation) and three-dimensional motion capture from trials where healthy adults walked overground. We found that OpenPose performed well in estimating many gait parameters (e.g., step time, step length, sagittal hip and knee angles) while some (e.g., double support time, sagittal ankle angles) were less accurate. We observed that mean values for individual participants – as are often of primary interest in clinical settings – were more accurate than individual step-by-step measurements. We also provide a workflow for users to perform their own gait analyses and offer suggestions and considerations for future approaches.

scholarly journals Two-dimensional video-based analysis of human gait using pose estimation

2021 ◽  
Vol 17 (4) ◽  
pp. e1008935
Author(s):  
Jan Stenum ◽  
Cristina Rossi ◽  
Ryan T. Roemmich
Keyword(s):  
Gait Analysis ◽  
Motion Capture ◽  
Pose Estimation ◽  
Mean Absolute Error ◽  
Three Dimensional ◽  
Absolute Error ◽  
Human Gait ◽  
Two Dimensional ◽  
Gait Parameters ◽  
Human Gait Analysis

Human gait analysis is often conducted in clinical and basic research, but many common approaches (e.g., three-dimensional motion capture, wearables) are expensive, immobile, data-limited, and require expertise. Recent advances in video-based pose estimation suggest potential for gait analysis using two-dimensional video collected from readily accessible devices (e.g., smartphones). To date, several studies have extracted features of human gait using markerless pose estimation. However, we currently lack evaluation of video-based approaches using a dataset of human gait for a wide range of gait parameters on a stride-by-stride basis and a workflow for performing gait analysis from video. Here, we compared spatiotemporal and sagittal kinematic gait parameters measured with OpenPose (open-source video-based human pose estimation) against simultaneously recorded three-dimensional motion capture from overground walking of healthy adults. When assessing all individual steps in the walking bouts, we observed mean absolute errors between motion capture and OpenPose of 0.02 s for temporal gait parameters (i.e., step time, stance time, swing time and double support time) and 0.049 m for step lengths. Accuracy improved when spatiotemporal gait parameters were calculated as individual participant mean values: mean absolute error was 0.01 s for temporal gait parameters and 0.018 m for step lengths. The greatest difference in gait speed between motion capture and OpenPose was less than 0.10 m s−1. Mean absolute error of sagittal plane hip, knee and ankle angles between motion capture and OpenPose were 4.0°, 5.6° and 7.4°. Our analysis workflow is freely available, involves minimal user input, and does not require prior gait analysis expertise. Finally, we offer suggestions and considerations for future applications of pose estimation for human gait analysis.

Two-dimensional video gait analysis: A systematic review of reliability, validity, and best practice considerations

2020 ◽  
Vol 44 (4) ◽  
pp. 245-262
Author(s):  
Alexandria Michelini ◽  
Arezoo Eshraghi ◽  
Jan Andrysek
Keyword(s):  
Systematic Review ◽  
Data Collection ◽  
Gait Analysis ◽  
Motion Capture ◽  
Study Design ◽  
Human Gait ◽  
Two Dimensional ◽  
Specific Patient ◽  
Gait Parameters ◽  
Motion Capture Systems

Background: Motion capture systems are widely used to quantify human gait. Two-dimensional (2D) video systems are simple to use, easily accessible, and affordable. However, their performance as compared to other systems (i.e. three-dimensional (3D) gait analysis) is not well established. Objectives: This work provides a comprehensive review of design specifications and performance characteristics (validity and reliability) of two-dimensional motion capture systems. Study design: Systematic review. Methods: A systematic literature search was conducted in three databases from 1990 to 2019 and identified 30 research articles that met the inclusion/exclusion criteria. Results: Reliability of measurements of two-dimensional video motion capture was found to vary greatly from poor to excellent. Results relating to validity were also highly variable. Comparisons between the studies were challenging due to differences in protocols, instrumentation, parameters assessed, and analyses performed. Conclusions: Variability in performance could be attributed to study design, gait parameters being measured, and technical aspects. The latter includes camera specifications (i.e. resolution and frame rate), setup (i.e. camera position), and analysis software. Given the variability in performance, additional validation testing may be needed for specific applications involving clinical or research-based assessments, including specific patient populations, gait parameters, mobility tasks, and data collection protocols. Clinical relevance This review article provides guidance on the application of 2D video gait analysis in a clinical or research setting. While not suitable in all instances, 2D gait analysis has promise in specific applications. Recommendations are provided about the patient populations, gait parameters, mobility tasks, and data collection protocols.

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