Measuring human movement for biomechanical applications using markerless motion capture

2006 ◽  
Author(s):  
Lars Mündermann ◽  
Stefano Corazza ◽  
Ajit M. Chaudhari ◽  
Thomas P. Andriacchi ◽  
Aravind Sundaresan ◽  
...  
Keyword(s):  
Motion Capture ◽  
Human Movement ◽  
Markerless Motion Capture

scholarly journals Evaluation of 3D markerless motion capture accuracy using OpenPose with multiple video cameras

2019 ◽  
Author(s):  
Nobuyasu Nakano ◽  
Tetsuro Sakura ◽  
Kazuhiro Ueda ◽  
Leon Omura ◽  
Arata Kimura ◽  
...  
Keyword(s):  
Motion Capture ◽  
Human Movement ◽  
Absolute Error ◽  
Motor Tasks ◽  
3D Pose Estimation ◽  
Video Cameras ◽  
Markerless Motion Capture ◽  
Optical Motion ◽  
Optical Motion Capture ◽  
Capture Technique

AbstractThere is a need within human movement sciences for a markerless motion capture system, which is easy to use and suffciently accurate to evaluate motor performance. This study aims to develop a 3D markerless motion capture technique, using OpenPose with multiple synchronized video cameras, and examine its accuracy in comparison with optical marker-based motion capture. Participants performed three motor tasks (walking, countermovement jumping, and ball throwing), with these movements measured using both marker-based optical motion capture and OpenPose-based markerless motion capture. The differences in corresponding joint positions, estimated from the two different methods throughout the analysis, were presented as a mean absolute error (MAE). The results demonstrated that, qualitatively, 3D pose estimation using markerless motion capture could correctly reproduce the movements of participants. Quantitatively, of all the mean absolute errors calculated, approximately 47% were less than 20 mm and 80% were less than 30 mm. However, 10% were greater than 40 mm. The primary reason for mean absolute errors exceeding 40mm was that OpenPose failed to track the participant’s pose in 2D images owing to failures, such as recognition of an object as a human body segment, or replacing one segment with another depending on the image of each frame. In conclusion, this study demonstrates that, if an algorithm that corrects all apparently wrong tracking can be incorporated into the system, OpenPose-based markerless motion capture can be used for human movement science with an accuracy of 30mm or less.

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

Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

2009 ◽  
Vol 87 (1-2) ◽  
pp. 156-169 ◽  
Author(s):  
Stefano Corazza ◽  
Lars Mündermann ◽  
Emiliano Gambaretto ◽  
Giancarlo Ferrigno ◽  
Thomas P. Andriacchi
Keyword(s):  
Motion Capture ◽  
Specific Model ◽  
Model Generation ◽  
Visual Hull ◽  
Markerless Motion Capture ◽  
Subject Specific

scholarly journals Staying Well Grounded in Markerless Motion Capture

2008 ◽  
pp. 385-395 ◽  
Author(s):  
Bodo Rosenhahn ◽  
Christian Schmaltz ◽  
Thomas Brox ◽  
Joachim Weickert ◽  
Hans-Peter Seidel
Keyword(s):  
Motion Capture ◽  
Markerless Motion Capture

The birth of motion capture: Transcribing the phenomena of bodily movement through the “graphic method”

2021 ◽  
pp. 263497952110403
Author(s):  
Mark Paterson
Keyword(s):  
20Th Century ◽  
Motion Capture ◽  
Animal Movement ◽  
Human Movement ◽  
Industrial Research ◽  
Early 20Th Century ◽  
Graphic Method ◽  
Bodily Movement ◽  
In The Wild

How is the movement of bodies recorded, traced, captured? How is the perception of movement decomposed, analyzed, and then reconstructed through signs, lines, and diagrams? This article traces how, with the help of engineers and collaborators, Etienne-Jules Marey’s self-styled “graphic method” innovated upon existing instruments and photographic apparatuses in order to capture not just the movement of horses’ legs but something of the biomechanical essence of animal movement through the technique of “chronophotographie.” Although inspired by Edward Muybridge’s photographs of horses in motion, for Marey the photographs were not the end result. What he achieved were new ways of transcribing the phenomena of bodily motion. Unlike previous physiologists who thrived on vivisection in the laboratory, Marey took ever greater pains to examine the principles of animal movement in the wild, and built an open-air “station physiologique” in a Parisian park for this purpose. One legacy of Marey’s chronophotographic technique was in the documentation and dissection of human movement, and became acknowledged precursors of the wave of Taylorism which would sweep industrial research in the early 20th century. But another legacy is the capacity to transcribe the phenomena of movement into other forms, externalizing perception across other media.

scholarly journals Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1801 ◽  
Author(s):  
Haitao Guo ◽  
Yunsick Sung
Keyword(s):  
Motion Capture ◽  
Short Term Memory ◽  
Arm Movements ◽  
Ground Truth ◽  
Human Movement ◽  
Human Motion ◽  
Soft Sensors ◽  
Upper Arm ◽  
Human Motion Capture ◽  
Gesture Control

The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM.

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