scholarly journals A Novel Coordinated Motion Fusion-Based Walking-Aid Robot System

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2761 ◽  
Author(s):  
Wenxia Xu ◽  
Jian Huang ◽  
Lei Cheng
Keyword(s):  
Human Locomotion ◽  
Detection Algorithm ◽  
Human Motion ◽  
Lower Limbs ◽  
Support Vector ◽  
Normal Walking ◽  
Coordinated Motion ◽  
Fusion Algorithm ◽  
Robot System ◽  
Walking Aid

Human locomotion is a coordinated motion between the upper and lower limbs, which should be considered in terms of both the user’s normal walking state and abnormal walking state for a walking-aid robot system. Therefore, a novel coordinated motion fusion-based walking-aid robot system was proposed. To develop the accurate human motion intention (HMI) of such robots when the user is in normal walking state, force-sensing resistor (FSR) sensors and a laser range finder (LRF) are used to detect the two HMIs expressed by the user’s upper and lower limbs. Then, a fuzzy logic control (FLC)-Kalman filter (LF)-based coordinated motion fusion algorithm is proposed to synthesize these two segmental HMIs to obtain an accurate HMI. A support vector machine (SVM)-based fall detection algorithm is used to detect whether the user is going to fall and to distinguish the user’s falling mode when he/she is in an abnormal walking state. The experimental results verify the effectiveness of the proposed algorithms.

scholarly journals Human Posture Detection Method Based on Wearable Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoou Li ◽  
Zhiyong Zhou ◽  
Jiajia Wu ◽  
Yichao Xiong
Keyword(s):  
Standard Deviation ◽  
Upper Limb ◽  
Plantar Pressure ◽  
Gait Recognition ◽  
Human Motion ◽  
Lower Limbs ◽  
Identification Accuracy ◽  
Support Vector ◽  
Human Posture ◽  
Semg Signals

The dynamic detection of human motion is important, which is widely applied in the fields of motion state capture and rehabilitation engineering. In this study, based on multimodal information of surface electromyography (sEMG) signals of upper limb and triaxial acceleration and plantar pressure signals of lower limb, the effective virtual driving control and gait recognition methods were proposed. The effective way of wearable human posture detection was also constructed. Firstly, the moving average window and threshold comparison were used to segment the sEMG signals of the upper limb. The standard deviation and singular values of wavelet coefficients were extracted as the features. After the training and classification by optimized support vector machine (SVM) algorithm, the real-time detection and analysis of three virtual driving actions were performed. The average identification accuracy was 90.90%. Secondly, the mean, standard deviation, variance, and wavelet energy spectrum of triaxial acceleration were extracted, and these parameters were combined with plantar pressure as the gait features. The optimized SVM was selected for the gait identification, and the average accuracy was 90.48%. The experimental results showed that, through different combinations of wearable sensors on the upper and lower limbs, the motion posture information could be dynamically detected, which could be used in the design of virtual rehabilitation system and walking auxiliary system.

Image fire detection algorithm based on support vector machine

2010 ◽  
Vol 30 (4) ◽  
pp. 1129-1131
Author(s):  
Na-juan YANG ◽  
Hui-qin WANG ◽  
Zong-fang MA
Keyword(s):  
Support Vector Machine ◽  
Fire Detection ◽  
Detection Algorithm ◽  
Support Vector

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals Statistical study of medium-scale traveling ionospheric disturbances in low-latitude ionosphere using an automatic algorithm

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Pin-Hsuan Cheng ◽  
Charles Lin ◽  
Yuichi Otsuka ◽  
Hanli Liu ◽  
Panthalingal Krishanunni Rajesh ◽  
...  
Keyword(s):  
Climate Model ◽  
Geomagnetic Latitude ◽  
Detection Algorithm ◽  
Support Vector ◽  
Electron Content ◽  
Ionospheric Disturbances ◽  
Low Latitude ◽  
Total Electron ◽  
Medium Scale ◽  
Traveling Ionospheric Disturbances

AbstractThis study investigates the medium-scale traveling ionospheric disturbances (MSTIDs) statistically at the low-latitude equatorial ionization anomaly (EIA) region in the northern hemisphere. We apply the automatic detection algorithm including the three-dimensional fast Fourier transform (3-D FFT) and support vector machine (SVM) on total electron content (TEC) observations, derived from a network of ground-based global navigation satellite system (GNSS) receivers in Taiwan (14.5° N geomagnetic latitude; 32.5° inclination), to identify MSTID from other waves or irregularity features. The obtained results are analyzed statistically to examine the behavior of low-latitude MSTIDs. Statistical results indicate the following characteristics. First, the southward (equatorward) MSTIDs are observed almost every day during 0800–2100 LT in Spring and Winter. At midnight, southward MSTIDs are more discernible in Summer and majority of them are propagating from Japan to Taiwan. Second, northward (poleward) MSTIDs are more frequently detected during 1200–2100 LT in Spring and Summer with the secondary peak of occurrence between day of year (DOY) 100–140 during 0000–0300 LT. The characteristics of the MSTIDs are interpreted with additional observations from radio occultation (RO) soundings of FORMOSAT-3/COSMIC as well as modeled atmospheric waves from the high-resolution Whole Atmosphere Community Climate Model (WACCM) suggesting that the nighttime MSTIDs in Summer is likely connected to the atmospheric gravity waves (AGWs).

Simulation Analysis of Foot Force in Athletes’ Training Process Based on Sports Biomechanics

2021 ◽  
Vol 7 (5) ◽  
pp. 4900-4913
Author(s):  
Li Huang ◽  
Jianqiu Hu
Keyword(s):  
Simulation Analysis ◽  
Rapid Development ◽  
Human Motion ◽  
Lower Limbs ◽  
Lower Extremity Injury ◽  
Foot Pressure ◽  
Modeling And Analysis ◽  
Analysis System ◽  
Foot Joint ◽  
Sports Biomechanics

Objectives: With the rapid development of sports biomechanics, a new frontier discipline, the modeling and Simulation of human motion, as one of the cutting-edge research topics of sports biomechanics, is receiving more and more attention.. Methods: Based on this, this paper provides theoretical support for the analysis and research of foot stress in the process of training and applies it to guiding practice by using the analysis technology based on sports biomechanics and the method of foot pressure and simulation modeling and analysis system. Results: The results of the study showed that the injury of the athletes in the lower limbs accounted for about 46.7%, followed by the injury of the upper limbs and the injury of the trunk. In the lower extremity injury, the most common part of the foot joint is about 28.1%. Conclusion: Studies have shown that the changes in the force of the athlete’s foot after fatigue have not had the good stability before, the duration of each stage of the completion of the movement is changing, and the control of the ankle joint is decreasing, which greatly increases the foot joint. The possibility of injury.

Search on Method of Netwok Intrusion Detection Based on Support Vector Machine with Pre-Extracting Support Vector

2013 ◽  
Vol 655-657 ◽  
pp. 1787-1790
Author(s):  
Sheng Chen Yu ◽  
Li Min Sun ◽  
Yang Xue ◽  
Hui Guo ◽  
Xiao Ju Wang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Detection Algorithm ◽  
Support Vector ◽  
Distance Ratio ◽  
Training Time ◽  
Training Examples ◽  
Center Distance

Intrusion detection algorithm based on support vector machine with pre-extracting support vector is proposed which combines the center distance ratio and classification algorithm. Given proper thresholds, we can use the support vector as a substitute for the training examples. Then the scale of dataset is decreased and the performance of support vector machine is improved in the detection rate and the training time. The experiment result has shown that the intrusion detection system(IDS) based on support vector machine with pre-extracting support needs less training time under the same detection performance condition.

scholarly journals An arc fault diagnosis algorithm using multiinformation fusion and support vector machines

2018 ◽  
Vol 5 (9) ◽  
pp. 180160 ◽  
Author(s):  
Jian-hong Yang ◽  
Huai-ying Fang ◽  
Ren-cheng Zhang ◽  
Kai Yang
Keyword(s):  
Low Voltage ◽  
Detection Algorithm ◽  
Recognition Algorithm ◽  
Support Vector ◽  
Signal Loss ◽  
Current Waveform ◽  
Accurate Identification ◽  
Metallic Contacts ◽  
Vector Machines ◽  
Integral Characteristics

Arc faults in low-voltage electrical circuits are the main hidden cause of electric fires. Accurate identification of arc faults is essential for safe power consumption. In this paper, a detection algorithm for arc faults is tested in a low-voltage circuit. With capacitance coupling and a logarithmic detector, the high-frequency radiation characteristics of arc faults can be extracted. A rapid method for computing the current waveform slope characteristics of an arc fault provides another characteristic. Current waveform periodic integral characteristics can be extracted according to asymmetries of the arc faults. These three characteristics are used to develop a detection algorithm of arc faults based on multiinformation fusion and support vector machine learning models. The tests indicated that for series arc faults with single and combination loads and for parallel arc faults between metallic contacts and along carbonization paths, the recognition algorithm could effectively avoid the problems of crosstalk and signal loss during arc fault detection.

scholarly journals Recognition of Human Emotion Detection and Annotation, using Local Descriptor and Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 2838-2843
Keyword(s):  
Support Vector Machine ◽  
Object Detection ◽  
Dynamic Environment ◽  
Detection Algorithm ◽  
Research Area ◽  
Multimedia Data ◽  
Video Annotation ◽  
Support Vector ◽  
People Detection ◽  
Local Descriptor

In multimedia data analysis, video tagging is the most challenging and active research area. In which finding or detecting the object with the dynamic environment is most challenging. Object detection and its validation are an essential functional step in video annotation. Considering the above challenges, the proposed system designed to presents the people detection module from a complex background. Detected persons are validated for further annotation process. Using publically available dataset for module design, Viola-Jones object detection algorithm is used for person detection. Support Vector Machine (SVM) authenticate the detected object/person based on it local features using Local Binary Pattern (LBP). The performance of the proposed system presents given architecture is effectively annotating the detected people emotion.

scholarly journals Recognition Technology of Athlete’s Limb Movement Combined Based on the Integrated Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Fei Tan ◽  
Xiaoqing Xie
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Learning Algorithm ◽  
Human Motion ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Recording Device ◽  
Table Tennis ◽  
Movement Recognition ◽  
Random Forest Tree

Human motion recognition based on inertial sensor is a new research direction in the field of pattern recognition. It carries out preprocessing, feature selection, and feature selection by placing inertial sensors on the surface of the human body. Finally, it mainly classifies and recognizes the extracted features of human action. There are many kinds of swing movements in table tennis. Accurately identifying these movement modes is of great significance for swing movement analysis. With the development of artificial intelligence technology, human movement recognition has made many breakthroughs in recent years, from machine learning to deep learning, from wearable sensors to visual sensors. However, there is not much work on movement recognition for table tennis, and the methods are still mainly integrated into the traditional field of machine learning. Therefore, this paper uses an acceleration sensor as a motion recording device for a table tennis disc and explores the three-axis acceleration data of four common swing motions. Traditional machine learning algorithms (decision tree, random forest tree, and support vector) are used to classify the swing motion, and a classification algorithm based on the idea of integration is designed. Experimental results show that the ensemble learning algorithm developed in this paper is better than the traditional machine learning algorithm, and the average recognition accuracy is 91%.

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