A New Scheme for Soft Tissue Artifact Compensation in Human Motion Analysis

2010 ◽  
Author(s):  
Bo Gao ◽  
Scott Banks ◽  
Nigel Zheng
Keyword(s):  
Soft Tissue ◽  
Motion Analysis ◽  
Normal Function ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Relative Movement ◽  
Non Invasive ◽  
Pathological Conditions ◽  
Skin Marker ◽  
Soft Tissue Artifact

Skin marker-based stereophotogrammetry provides a non-invasive and radiation-free approach in human motion analysis. It has been widely used to study the normal function and pathological conditions of human musculoskeletal system. One major limitation of this technique is usually referred to as soft tissue artifact (STA), which is defined as the relative movement between skin markers and the underlying bone. Much effort has been devoted to developing techniques for STA compensation and better motion analysis accuracy. However, the problem has not yet been solved satisfactorily.

Quantification of soft tissue artifact in lower limb human motion analysis: A systematic review

2010 ◽  
Vol 31 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Alana Peters ◽  
Brook Galna ◽  
Morgan Sangeux ◽  
Meg Morris ◽  
Richard Baker
Keyword(s):  
Systematic Review ◽  
Soft Tissue ◽  
Motion Analysis ◽  
Lower Limb ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Soft Tissue Artifact

QUANTIFICATION OF THREE-DIMENSIONAL MOVEMENT OF SKIN MARKERS RELATIVE TO THE UNDERLYING BONES DURING FUNCTIONAL ACTIVITIES

2009 ◽  
Vol 21 (03) ◽  
pp. 223-232 ◽  
Author(s):  
Tsung-Yuan Tsai ◽  
Tung-Wu Lu ◽  
Mei-Ying Kuo ◽  
Horng-Chaung Hsu
Keyword(s):  
Motion Analysis ◽  
Three Dimensional ◽  
Normal Subjects ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Location Selection ◽  
Functional Activities ◽  
Skin Marker ◽  
Compensation Approach

Skin marker-based stereophotogrammetry has been widely used in the in vivo, noninvasive measurement of three-dimensional (3D) joint kinematics in many clinical applications. However, the measured poses of body segments are subject to errors called soft tissue artifacts (STA). No study has reported the unrestricted STA of markers on the thigh and shank in normal subjects during functional activities. The purpose of this study was to assess the 3D movement of skin markers relative to the underlying bones in normal subjects during functional activities using a noninvasive method based on the integration of 3D fluoroscopy and stereophotogrammetry. Generally, thigh markers had greater STA than shank ones and the STA of the markers were in nonlinear relationships with knee flexion angles. The STA of a marker also appeared to vary among subjects and were affected by activities. This suggests that correction of STA in human motion analysis may have to consider the multijoint nature of functional activities such as using a global compensation approach with individual anthropometric data. The results of the current study may be helpful for establishing guidelines of marker location selection and for developing STA compensation methods in human motion analysis.

scholarly journals The Potential of Computer Vision-Based Marker-Less Human Motion Analysis for Rehabilitation

2021 ◽  
Vol 10 ◽  
pp. 117957272110223
Author(s):  
Thomas Hellsten ◽  
Jonny Karlsson ◽  
Muhammed Shamsuzzaman ◽  
Göran Pulkkis
Keyword(s):  
Computer Vision ◽  
Motion Analysis ◽  
Pose Estimation ◽  
Joint Angle ◽  
Human Motion ◽  
Human Pose Estimation ◽  
Human Motion Analysis ◽  
Single View ◽  
Recent Computer ◽  
Human Pose

Background: Several factors, including the aging population and the recent corona pandemic, have increased the need for cost effective, easy-to-use and reliable telerehabilitation services. Computer vision-based marker-less human pose estimation is a promising variant of telerehabilitation and is currently an intensive research topic. It has attracted significant interest for detailed motion analysis, as it does not need arrangement of external fiducials while capturing motion data from images. This is promising for rehabilitation applications, as they enable analysis and supervision of clients’ exercises and reduce clients’ need for visiting physiotherapists in person. However, development of a marker-less motion analysis system with precise accuracy for joint identification, joint angle measurements and advanced motion analysis is an open challenge. Objectives: The main objective of this paper is to provide a critical overview of recent computer vision-based marker-less human pose estimation systems and their applicability for rehabilitation application. An overview of some existing marker-less rehabilitation applications is also provided. Methods: This paper presents a critical review of recent computer vision-based marker-less human pose estimation systems with focus on their provided joint localization accuracy in comparison to physiotherapy requirements and ease of use. The accuracy, in terms of the capability to measure the knee angle, is analysed using simulation. Results: Current pose estimation systems use 2D, 3D, multiple and single view-based techniques. The most promising techniques from a physiotherapy point of view are 3D marker-less pose estimation based on a single view as these can perform advanced motion analysis of the human body while only requiring a single camera and a computing device. Preliminary simulations reveal that some proposed systems already provide a sufficient accuracy for 2D joint angle estimations. Conclusions: Even though test results of different applications for some proposed techniques are promising, more rigour testing is required for validating their accuracy before they can be widely adopted in advanced rehabilitation applications.

Video-Based Human Motion Analysis

2011 ◽  
Vol 403-408 ◽  
pp. 2593-2597
Author(s):  
Hong Bao ◽  
Zhi Min Liu
Keyword(s):  
Motion Analysis ◽  
Human Body ◽  
Body Shape ◽  
Human Motion ◽  
Random Motion ◽  
Human Motion Analysis ◽  
Mass Center ◽  
Shape Features ◽  
Skeleton Model ◽  
Regular Motion

In the analysis of human motion, movement was divided into regular motion (such as walking and running) and random motion (such as falling down).Human skeleton model is used in this paper to do the video-based analysis. Key joints on human body were chosen to be traced instead of tracking the entire human body. Shape features like mass center trajectory were used to describe the movement, and to classify human motion. desired results achieved.

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