scholarly journals Research on Human Motion Analysis in Moving Scene Based on Timing Detection and Video Description Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Quanping Shen ◽  
Songzhong Ye
Keyword(s):  
Domain Knowledge ◽  
Movement Analysis ◽  
Recall Rate ◽  
Detection Algorithm ◽  
Human Motion ◽  
Small Scale ◽  
Human Motion Analysis ◽  
Action Detection ◽  
Target Action ◽  
Temporal Action

Technical movement analysis requires specialized domain knowledge and processing a large amount of data, and the advantages of AI in processing data can improve the efficiency of data analysis. In this paper, we propose a feature pyramid network-based temporal action detection (FPN-TAD) algorithm, which is used to solve the problem that the action proposal module has a low recall rate for small-scale temporal target action regions in the current video temporal action detection algorithm research. This paper is divided into three parts. The first part is an overview of the algorithm; the second part elaborates the network structure and the working principle of the FPN-TAD algorithm; and the third part gives the experimental results and analysis of the algorithm.

scholarly journals Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation

2021 ◽  
Vol 8 ◽  
Author(s):  
Honghu Xue ◽  
Rebecca Herzog ◽  
Till M. Berger ◽  
Tobias Bäumer ◽  
Anne Weissbach ◽  
...  
Keyword(s):  
Domain Knowledge ◽  
High Efficiency ◽  
Initial Result ◽  
Human Motion ◽  
Skill Learning ◽  
Human Motion Analysis ◽  
Specific Domain ◽  
Movement Primitives ◽  
Analysis Computer ◽  
Human Motions

In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors, or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives (ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with 10 participants. The results validate ProMPs as a robust and effective feature extractor for human motions.

scholarly journals Analysis and Research on Human Movement in Sports Scene

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yan Wang ◽  
Yuchen Zhang ◽  
LinJun Shen ◽  
ShuMing Wang
Keyword(s):  
Body Movement ◽  
Movement Analysis ◽  
Human Movement ◽  
Human Motion ◽  
Whole Body ◽  
Human Motion Analysis ◽  
Analysis Method ◽  
Multilabel Classification ◽  
Movement Process ◽  
Rope Skipping

As a whole-body sport, skipping rope plays an increasingly important role in daily life. In rope-skipping education, due to the lack of professional teachers, the training efficiency of students is low. The rope-skipping monitoring device is heavy and expensive, and the cost of labor statistics and energy consumption are high. In order to quickly analyze the movement process of students and provide correct guidance, this article implements the movement analysis method of the human body movement process. The problem of limb posture analysis in rope skipping is transformed into a multilabel classification problem, a real-time human motion analysis method based on mobile vision is proposed, and the algorithm model is verified in the rope-skipping scene. The experimental results prove that this paper proposes the improved algorithm, which achieved the expected effect. In the analysis of rope-skipping action, the choice of hyperparameters during the experiment is introduced, and it is verified that the proposed ALSTM-LSTM can solve the problem of multilabel classification in the rope-skipping process. The accuracy rate reaches 95.1%, and it can provide the best in all indicators and good performance. It is of great significance for movement analysis and movement quality evaluation during exercise.

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.

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.

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