scholarly journals Car in a dangerous motion detection method by means of wavelet analysis

2007 ◽  
Vol 27 (Supplement1) ◽  
pp. 217-220 ◽  
Author(s):  
Kohei ARAI ◽  
Tomoko NISHIKAWA
Keyword(s):  
Wavelet Analysis ◽  
Motion Detection ◽  
Detection Method

A Detection Method for Traffic Incident Using Wavelet Analysis

2000 ◽  
Vol 33 (9) ◽  
pp. 241-245
Author(s):  
Shoufeng Ma ◽  
Guoguang He ◽  
Guizhu Wang
Keyword(s):  
Wavelet Analysis ◽  
Detection Method ◽  
Traffic Incident

Detection of Human Body Motion Based on Electrostatic Measurements in the Indoor Environment

2011 ◽  
Vol 480-481 ◽  
pp. 1329-1334
Author(s):  
Wei Zheng ◽  
Zhan Zhong Cui
Keyword(s):  
Theoretical Analysis ◽  
Real Time ◽  
Human Body ◽  
Motion Detection ◽  
Average Velocity ◽  
Indoor Environment ◽  
Detection Method ◽  
Body Motion ◽  
The Real ◽  
Multiple Electrodes

An effective non-contact electrostatic detection method is used for human body motion detection. Theoretical analysis and pratical experiments are carried out to prove that this method is effective in the field of human body monitoring, in which a model for human body induced potential by stepping has been proposed. Furthermore, experiment results also prove that it’s feasible to measure the average velocity and route of human body motion by multiple electrodes array. What’s more the real-time velocity and direction of human body motion can be determined by orthogonal electrostatic detector array, and the real-time velocity and direction of human body motion can be obtained within the range of 2 meters.

scholarly journals A comparative study of motion detection with FMG and sEMG methods for assistive applications

2020 ◽  
Vol 7 ◽  
pp. 205566832093858
Author(s):  
Muhammad Raza Ul Islam ◽  
Asim Waris ◽  
Ernest Nlandu Kamavuako ◽  
Shaoping Bai
Keyword(s):  
Motion Detection ◽  
Classification Accuracy ◽  
Detection Method ◽  
Daily Basis ◽  
Detection Methods ◽  
Feed Forward Neural Network ◽  
Training Scheme ◽  
Training Schemes ◽  
And Training

Introduction While surface-electromyography (sEMG) has been widely used in limb motion detection for the control of exoskeleton, there is an increasing interest to use forcemyography (FMG) method to detect motion. In this paper, we review the applications of two types of motion detection methods. Their performances were experimentally compared in day-to-day classification of forearm motions. The objective is to select a detection method suitable for motion assistance on a daily basis. Methods Comparisons of motion detection with FMG and sEMG were carried out considering classification accuracy (CA), repeatability and training scheme. For both methods, classification of motions was achieved through feed-forward neural network. Repeatability was evaluated on the basis of change in CA between days and also training schemes. Results The experiments shows that day-to-day CA with FMG can reach 84.9%, compared with a CA of 77.8% with sEMG, when the classifiers were trained only on the first day. Moreover, the CA with FMG can reach to 86.5%, comparable to CA of 84.1% with sEMG, if classifiers were trained daily. Conclusions Results suggest that data recorded from FMG is more repeatable in day-to-day testing and therefore FMG-based methods can be more useful than sEMG-based methods for motion detection in applications where exoskeletons are used as needed on a daily basis.

scholarly journals Motion Detection Enhanced Control of an Upper Limb Exoskeleton Robot for Rehabilitation Training

2017 ◽  
Vol 14 (01) ◽  
pp. 1650031 ◽  
Author(s):  
Wenjun Ye ◽  
Zhijun Li ◽  
Chenguang Yang ◽  
Fei Chen ◽  
Chun-Yi Su
Keyword(s):  
Motion Detection ◽  
Detection Method ◽  
Control Method ◽  
Control Design ◽  
Training Process ◽  
User Intention ◽  
Upper Limb Exoskeleton ◽  
Exoskeleton Robot ◽  
Control Scheme ◽  
Adaptive Control Scheme

The paper studies the control design of an exoskeleton robot based on electromyography (EMG). An EMG-based motion detection method is proposed to trigger the rehabilitation assistance according to user intention. An adaptive control scheme that compensates for the exoskeleton's dynamics is employed, and it is able to provide assistance tailored to the human user, who is supposed to participate actively in the training process. The experiment results verify the effectiveness of the control method developed in this paper.

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