Development of Proposed Bi-Modal Human Motion Classification System

2014 ◽  
Vol 654 ◽  
pp. 315-320
Author(s):  
Ching Yee Yong ◽  
Rubita Sudirman ◽  
Kim Mey Chew
Keyword(s):  
Human Activities ◽  
Elevation Angle ◽  
Image Data ◽  
Evaluation Process ◽  
Transformation Method ◽  
Human Motion ◽  
Learning System ◽  
Human Motion Analysis ◽  
Wearable Sensor ◽  
Motion Data

The main objective of this study is to discover and investigate greater levels of human motion activities recognition. The study presents four approaches of human motion data processing to recognize the human activities. Data collection process was performed in two ways: wearable sensor based in signal data and vision based in image data. The proposed approaches used to analyze the signal and image data are: wearable sensor using 3-space sensing with angular velocity and elevation angle as moderators, wearable sensor using statistical nine existing and a proposed developed classifiers as classification learning system, vision based using skeletonization with humerus-radius and horizontal-radius as measuring angle and vision based image-signal histogram using 2D-1D transformation method. The principal contributions of this thesis are the development of the human motion analysis methods with validated evaluation process tested on the proposed systems. The proposed systems achieved more than 98 % for signal processing and 97 % for image processing of accuracy on recognizing human activities.

Adaptive Animation of Human Motion for E-Learning Applications

2011 ◽  
pp. 134-144
Author(s):  
Frederick W.B. Li ◽  
Rynson W.H. Lau ◽  
Taku Komura ◽  
Meng Wang ◽  
Becky Siu
Keyword(s):  
Human Motion ◽  
Learning System ◽  
The Internet ◽  
Motion Information ◽  
User Interest ◽  
Motion Data ◽  
Network Bandwidth ◽  
Levels Of Detail ◽  
E Learning ◽  
Human Motions

Human motion animation has been one of the major research topics in the field of computer graphics for decades. Techniques developed in this area help present human motions in various applications. This is crucial for enhancing the realism as well as promoting the user interest in the applications. To carry this merit to e-learning applications, we have developed efficient techniques for delivering human motion information over the Internet to collaborating e-learning users and revealing the motion information in the client machines with different rendering capability. Our method offers a mechanism to extract human motion data at various levels of detail (LoD). We also propose a set of importance factors to allow an e-learning system to determine the LoD of the human motion for rendering as well as transmission, according to the importance of the motion and the available network bandwidth. At the end of the paper, we demonstrate the effectiveness of the new method with some experimental results.

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.

Saving and Reproduction of Human Motion Data by Using Haptic Devices with Different Configurations

2011 ◽  
Vol 131 (3) ◽  
pp. 267-274 ◽  
Author(s):  
Noboru Tsunashima ◽  
Yuki Yokokura ◽  
Seiichiro Katsura
Keyword(s):  
Human Motion ◽  
Haptic Devices ◽  
Motion Data

scholarly journals Acceleration Feature Extraction of Human Body Based on Wearable Devices

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 924
Author(s):  
Zhenzhen Huang ◽  
Qiang Niu ◽  
Ilsun You ◽  
Giovanni Pau
Keyword(s):  
Genetic Algorithm ◽  
Feature Extraction ◽  
Kalman Filter ◽  
Human Body ◽  
Wearable Devices ◽  
Human Motion ◽  
Data Set ◽  
Feature Extraction Method ◽  
Motion Data ◽  
Kalman Filter Algorithm

Wearable devices used for human body monitoring has broad applications in smart home, sports, security and other fields. Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human body acceleration feature extraction method based on wearable devices is studied. Firstly, Butterworth filter is used to filter the data. Then, in order to ensure the extracted feature value more accurately, it is necessary to remove the abnormal data in the source. This paper combines Kalman filter algorithm with a genetic algorithm and use the genetic algorithm to code the parameters of the Kalman filter algorithm. We use Standard Deviation (SD), Interval of Peaks (IoP) and Difference between Adjacent Peaks and Troughs (DAPT) to analyze seven kinds of acceleration. At last, SisFall data set, which is a globally available data set for study and experiments, is used for experiments to verify the effectiveness of our method. Based on simulation results, we can conclude that our method can distinguish different activity clearly.

Sign in / Sign up

Export Citation Format

Share Document