Human Gait Analysis Using OpenPose

Viswakumar, Aditya; Rajagopalan, Venkateswaran; Ray, Tathagata; Parimi, Chandu

doi:10.1109/iciip47207.2019.8985781

Cited by 73 publications

(45 citation statements)

References 13 publications

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“…Liao et al [ 26 ] have not released their code, but they used OpenPose outputs to train a model invariant to view. Viswakumar et al [ 27 ] performed direct calculation of the knee angle from an average phone camera processed by OpenPose. They showed that OpenPose is robust to challenging clothing such as large Indian pants, as well as to extreme lightning conditions.…”

Section: Introductionmentioning

confidence: 99%

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness

Pagnon

Domalain

Revéret

2021

Sensors

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Being able to capture relevant information about elite athletes’ movement “in the wild” is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7° and 3.2° for all conditions and tasks, and mean absolute errors (compared to the reference condition—Ref) ranged between 0.35° and 1.6°. For walking, errors in the sagittal plane were: 1.5°, 0.90°, 0.19° for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.

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Section: Introductionmentioning

confidence: 99%

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness

Pagnon

Domalain

Revéret

2021

Sensors

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“…We suggest that separate analyses of a range of gait pathologies are needed to establish the accuracy obtained with our workflow. Some prior studies have used OpenPose to investigate particular features of walking or other human movement patterns [11,12,14,15,[26][27][28]. Our findings align with these reports in that we found OpenPose to show promise in providing quantitative information about human movement (in our case, walking).…”

Section: Plos Computational Biologysupporting

confidence: 89%

“…Several prior studies have extracted features of human gait using markerless pose estimation [11][12][13][14][15][16][17]; however, there remains a critical need for comparisons of these techniques against simultaneously collected, gold-standard measurements. We regard the following considerations as imperative to evaluate the performance of markerless pose estimation for human gait analysis: 1) use of a dataset containing overground walking sequences with synchronized three-dimensional motion capture and video recordings, including video recordings from multiple perspectives, 2) stride-by-stride comparisons of gait parameters in addition to comparisons of average gait parameters across walking bouts for individual participants, 3) comparisons of a wide range of gait parameters including spatiotemporal and kinematic measures and 4) an approach with no need for additional network processing.…”

Section: Introductionmentioning

confidence: 99%

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“…(b) The COCO model is a collection of 18 points including some facial key points. (c) BODY pose provides 25 points consisting of COCO + feet keypoints [30,31]. Detectron2 was built by Facebook AI Research (FAIR) to support the rapid implementation and evaluation of novel computer vision research.…”

Section: Human Pose Detection and Body Feature Extraction: A State Of The Artmentioning

confidence: 99%

Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

et al. 2021

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Assistance and rehabilitation robotic platforms must have precise sensory systems for human–robot interaction. Therefore, human pose estimation is a current topic of research, especially for the safety of human–robot collaboration and the evaluation of human biomarkers. Within this field of research, the evaluation of the low-cost marker-less human pose estimators of OpenPose and Detectron 2 has received much attention for their diversity of applications, such as surveillance, sports, videogames, and assessment in human motor rehabilitation. This work aimed to evaluate and compare the angles in the elbow and shoulder joints estimated by OpenPose and Detectron 2 during four typical upper-limb rehabilitation exercises: elbow side flexion, elbow flexion, shoulder extension, and shoulder abduction. A setup of two Kinect 2 RGBD cameras was used to obtain the ground truth of the joint and skeleton estimations during the different exercises. Finally, we provided a numerical comparison (RMSE and MAE) among the angle measurements obtained with OpenPose, Detectron 2, and the ground truth. The results showed how OpenPose outperforms Detectron 2 in these types of applications.

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Human Gait Analysis Using OpenPose

Cited by 73 publications

References 13 publications

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness

Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness

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Human Pose Detection for Robotic-Assisted and Rehabilitation Environments

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