scholarly journals A review of computer vision-based approaches for physical rehabilitation and assessment

2021 ◽  
Author(s):  
Bappaditya Debnath ◽  
Mary O’Brien ◽  
Motonori Yamaguchi ◽  
Ardhendu Behera
Keyword(s):  
Computer Vision ◽  
Human Motion ◽  
Physical Rehabilitation ◽  
Human Motion Analysis ◽  
Functional Rehabilitation ◽  
Physical Impairments ◽  
The Past ◽  
Future Direction ◽  
Assessment Research ◽  
Vision Application

AbstractThe computer vision community has extensively researched the area of human motion analysis, which primarily focuses on pose estimation, activity recognition, pose or gesture recognition and so on. However for many applications, like monitoring of functional rehabilitation of patients with musculo skeletal or physical impairments, the requirement is to comparatively evaluate human motion. In this survey, we capture important literature on vision-based monitoring and physical rehabilitation that focuses on comparative evaluation of human motion during the past two decades and discuss the state of current research in this area. Unlike other reviews in this area, which are written from a clinical objective, this article presents research in this area from a computer vision application perspective. We propose our own taxonomy of computer vision-based rehabilitation and assessment research which are further divided into sub-categories to capture novelties of each research. The review discusses the challenges of this domain due to the wide ranging human motion abnormalities and difficulty in automatically assessing those abnormalities. Finally, suggestions on the future direction of research are offered.

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 Motion Capture for Measuring Human Movement

2011 ◽  
pp. 81-100 ◽  
Author(s):  
Chee Kwang Quah ◽  
Michael Koh ◽  
Alex Ong ◽  
Hock Soon Seah ◽  
Andre Gagalowicz
Keyword(s):  
Computer Vision ◽  
Digital Media ◽  
Motion Capture ◽  
Human Movement ◽  
Human Motion ◽  
The Body ◽  
Optical Systems ◽  
Human Motion Analysis ◽  
Capture Method ◽  
The Subject

Through the advancement of electronics technologies, human motion analysis applications span many domains. Existing commercially available magnetic, mechanical and optical systems for motion capture and analyses are far from being able to operate in natural scenarios and environments. The current shortcoming of requiring the subject to wear sensors and markers on the body has prompted development directed towards a marker-less setup using computer vision approaches. However, there are still many challenges and problems in computer vision methods such as inconsistency of illumination, occlusion and lack of understanding and representation of its operating scenario. The authors present a videobased marker-less motion capture method that has the potential to operate in natural scenarios such as occlusive and cluttered scenes. In specific applications in sports biomechanics and education, which are stimulated by the usage of interactive digital media and augmented reality, accurate and reliable capture of human motion are essential.

Human motion analysis and action scoring technology for sports training based on computer vision features

2021 ◽  
pp. 1-9
Author(s):  
Yong Bai ◽  
Yinggang Chen
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Digital Image Processing ◽  
Human Body ◽  
Median Filter ◽  
Human Movement ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Recognition Model ◽  
Frame Difference

With the advent of the information age, computer-related application research has become more and more extensive, human motion analysis and action scoring based on computer vision have gradually become the focus of attention. In order to adapt to the development of the times and solve the problems related to the analysis of human motion, the experiment analyzed the similarity of eight common human movement behaviors, analyze the movement speed of men and women under sports training, and analyzed the accuracy of the human body motion recognition model in the two cases of the original gray data and the frame difference channel, finally, the denoising performance of four different algorithms of SMF, EMF, RAMF and median filter algorithm in digital image processing is analyzed. The final result shows that there is a big similarity between the same kind of human movement behavior, the accuracy rate of the frame difference channel human body recognition model is higher than that of the original gray data recognition model, and digital image processing median filter algorithm has good image denoising performance.

Video-Based Motion Capture for Measuring Human Movement

2011 ◽  
pp. 209-228
Author(s):  
Chee Kwang Quah ◽  
Michael Koh ◽  
Alex Ong ◽  
Hock Soon Seah ◽  
Andre Gagalowicz
Keyword(s):  
Computer Vision ◽  
Digital Media ◽  
Motion Capture ◽  
Human Movement ◽  
Human Motion ◽  
The Body ◽  
Optical Systems ◽  
Human Motion Analysis ◽  
Capture Method ◽  
The Subject

Through the advancement of electronics technologies, human motion analysis applications span many domains. Existing commercially available magnetic, mechanical and optical systems for motion capture and analyses are far from being able to operate in natural scenarios and environments. The current shortcoming of requiring the subject to wear sensors and markers on the body has prompted development directed towards a marker-less setup using computer vision approaches. However, there are still many challenges and problems in computer vision methods such as inconsistency of illumination, occlusion and lack of understanding and representation of its operating scenario. The authors present a videobased marker-less motion capture method that has the potential to operate in natural scenarios such as occlusive and cluttered scenes. In specific applications in sports biomechanics and education, which are stimulated by the usage of interactive digital media and augmented reality, accurate and reliable capture of human motion are essential.

scholarly journals Computer-vision aided functional movement measurement in people with and without axial spondyloarthritis – validation and feasibility study protocol

2021 ◽  
Author(s):  
Erin Hannink ◽  
Maedeh Mansoubi ◽  
Neil Cronin ◽  
Benjamin Waller ◽  
Helen Dawes
Keyword(s):  
Computer Vision ◽  
Back Pain ◽  
Chronic Back Pain ◽  
Axial Spondyloarthritis ◽  
Human Movement ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Validity And Reliability ◽  
Home Environments ◽  
Functional Movement

Back pain is a common form of disability worldwide, and one condition that causes chronic back pain is axial spondyloarthritis (axSpA) which primarily affects spinal joints resulting in pain and joint stiffness. Markerless human motion analysis uses a computer-vision (CV) aided system to automate human movement from videos. In this protocol, the study will aim to estimate criterion validity and reliability of functional movement measurement using a CV-aided system by comparing it to a standard clinical measurement; secondarily, to assess the feasibility of the CV-aided system in the lab and home environments. An index of tests of functional movement, range of motion and posture will be captured on video and measured using the CV-aided system in the lab and home environments. The index of tests will be compared to measurement performed by an experienced physiotherapist. Bland-Altman plots will be used to determine agreement between the methods, and reliability and completion rates will be used to determine the feasibility of the CV-aided system.

scholarly journals HUMAN MOTION ANALYSIS, RECOGNITION AND UNDERSTANDING IN COMPUTER VISION: A REVIEW

2013 ◽  
Vol 41 (5) ◽  
pp. 1928-1946
Author(s):  
Ahmed Nabil Mohamed ◽  
Mohamed Moanes Ali
Keyword(s):  
Computer Vision ◽  
Motion Analysis ◽  
Human Motion ◽  
Human Motion Analysis

A Conversation on Student Affairs Scholarship

NASPA Journal ◽  
2002 ◽  
Vol 39 (2) ◽  
Author(s):  
Larry D. Roper
Keyword(s):  
Student Affairs ◽  
Electronic Format ◽  
The Past ◽  
The Future ◽  
Journal Editorial ◽  
Future Direction

For the past 18 months the NASPA Journal Editorial Board has been engaged in an ongoing conversation about the future direction of the Journal. Among the issues we have discussed are: What should comprise the content of the Journal?, How do we decide when or if we will move the Journal to an electronic format?, What do our members want in the Journal?, and What type of scholarship should we be publishing? The last question — What type of scholarship should we be publishing? — led to an energetic conversation within the Editorial Board.

Human motion analysis using expressions of non-separated accelerometer values as character strings

2021 ◽  
Author(s):  
Kosuke Shima ◽  
Atsuko Mutoh ◽  
Koichi Moriyama ◽  
Nobuhiro Inuzuka
Keyword(s):  
Motion Analysis ◽  
Human Motion ◽  
Human Motion Analysis
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