On unifying deep learning and edge computing for human motion analysis in exergames development

2021 ◽  
Author(s):  
Antonis Pardos ◽  
Andreas Menychtas ◽  
Ilias Maglogiannis
Keyword(s):  
Deep Learning ◽  
Motion Analysis ◽  
Human Motion ◽  
Edge Computing ◽  
Human Motion Analysis

A systematic Approach to Human Motion Recognition using Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1996-2005
Keyword(s):  
Deep Learning ◽  
Motion Analysis ◽  
Systematic Approach ◽  
Performance Metrics ◽  
Modern Technology ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Motion Recognition ◽  
Motion Data ◽  
Human Motion Recognition

The application of Human Motion Analysis (HMA) under Computer Vision (CV) is an emerging field which entails various applications such as gait analysis, behavioural cloning and animation of motion, intent detection, etc. For such motion analysis various open source datasets have been created that help analyze motion behaviour. Motion Capture (mocap) files have been used extensively to store motion data and analyze them. Although the weightage of these applications can be huge in modern technology, not much work on human motion recognition has been done using mocap datasets. In this paper, we propose a systematic approach to human motion recognition using software engineering, data analysis and deep learning algorithms. A Deep Learning (DL) model using Gated Recurrent Network (GRU) for the classification of human motion. CMU mocap dataset is used for analyzing motion data and modelling the DL framework. The trained algorithm is tested using accuracy and Mean Absolute Error (MAE) and a user live feed as performance metrics. A 90.1% validation accuracy is obtained on final evaluation.

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

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.

Vision-Assisted Human Motion Analysis for Bed Exit Prediction Model Construction

2017 ◽  
Author(s):  
Tian-Xiang Chen ◽  
Rong-Shue Hsiao ◽  
Chun-Hao Kao ◽  
Hsin - Piao Lin ◽  
Shiann-Shiun Jeng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Motion Analysis ◽  
Human Motion ◽  
Model Construction ◽  
Human Motion Analysis

Fuzzy Qualitative Human Motion Analysis

2017 ◽  
pp. 67-93
Author(s):  
Honghai Liu ◽  
Zhaojie Ju ◽  
Xiaofei Ji ◽  
Chee Seng Chan ◽  
Mehdi Khoury
Keyword(s):  
Motion Analysis ◽  
Human Motion ◽  
Human Motion Analysis
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