STUDY OF GAIT PATTERN RECOGNITION BASED ON FUSION OF MECHANOMYOGRAPHY AND ATTITUDE ANGLE SIGNAL

2020 ◽  
Vol 20 (02) ◽  
pp. 1950085 ◽  
Author(s):  
JING YU ◽  
YUE ZHANG ◽  
CHUNMING XIA
Keyword(s):  
Vastus Lateralis ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Rectus Femoris ◽  
Gait Pattern ◽  
Support Vector ◽  
Dynamic Transition ◽  
Transition Activities ◽  
Attitude Angle ◽  
Average Recognition Rate

The study of lower limb movements plays an important role in many fields, such as rehabilitation and treatment of disabled patients, detection, and monitoring of daily life, as well as the interaction between people and machine, like the application of intelligent prosthetics. In this paper, the wireless device was used to collect the mechanomyography (MMG) signals of four thigh muscles (rectus femoris, vastus lateralis, vastus medialis, and semitendinosus) and the attitude angle of rectus femoris. High precision was achieved in 11 gait movements, including 3 static activities, 4 dynamic transition activities, and 4 dynamic activities. It has been verified that the hidden Markov model (HMM) could not only be applied to the MMG-based gait recognition with high veracity but also support comparative analysis between support vector machine (SVM) and quadratic discriminant analysis (QDA). In addition, the experiment was conducted from the perspectives of feature selections, channel combinations, and muscle contribution rates. The results show that the average classification accuracy of dynamic motions based on MMG is 98.27%, while based on attitude angle, the average recognition rate of static motions and dynamic transition motions could achieve 98.33% and 100%, respectively. Generally, the average recognition rate of 11 gait motions is 98.91%.

scholarly journals Multimodal Few-Shot Learning for Gait Recognition

2020 ◽  
Vol 10 (21) ◽  
pp. 7619
Author(s):  
Jucheol Moon ◽  
Nhat Anh Le ◽  
Nelson Hebert Minaya ◽  
Sang-Il Choi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Recognition Problem ◽  
Human Gait ◽  
Training Dataset ◽  
Support Vector ◽  
Open Set ◽  
Latent Space

A person’s gait is a behavioral trait that is uniquely associated with each individual and can be used to recognize the person. As information about the human gait can be captured by wearable devices, a few studies have led to the proposal of methods to process gait information for identification purposes. Despite recent advances in gait recognition, an open set gait recognition problem presents challenges to current approaches. To address the open set gait recognition problem, a system should be able to deal with unseen subjects who have not included in the training dataset. In this paper, we propose a system that learns a mapping from a multimodal time series collected using insole to a latent (embedding vector) space to address the open set gait recognition problem. The distance between two embedding vectors in the latent space corresponds to the similarity between two multimodal time series. Using the characteristics of the human gait pattern, multimodal time series are sliced into unit steps. The system maps unit steps to embedding vectors using an ensemble consisting of a convolutional neural network and a recurrent neural network. To recognize each individual, the system learns a decision function using a one-class support vector machine from a few embedding vectors of the person in the latent space, then the system determines whether an unknown unit step is recognized as belonging to a known individual. Our experiments demonstrate that the proposed framework recognizes individuals with high accuracy regardless they have been registered or not. If we could have an environment in which all people would be wearing the insole, the framework would be used for user verification widely.

Research on Indicator Diagram of Shock Absorber Fault Recognition Based on Support Vector Machine

2013 ◽  
Vol 765-767 ◽  
pp. 2195-2198
Author(s):  
Wei Dong Xie ◽  
Kan Gao ◽  
Ji Sheng Shen
Keyword(s):  
Support Vector Machine ◽  
Shock Absorber ◽  
Recognition Rate ◽  
Support Vector ◽  
Indicator Diagram ◽  
Fault Recognition ◽  
Average Recognition Rate ◽  
Rbf Kernel ◽  
On Line ◽  
Rbf Kernel Function

In order to meet the development of shock absorber on-line detection, a new method of indicator diagrams recognition for shock absorber based on support vector machine (SVM) is proposed. Different fault patterns of shock absorber indicator diagram are discussed, including their main causes. The recognition model is constructed each with Linear, Polynomial and Radial Basis Function (RBF) kernel function. The experimental results show that the best average recognition rate is 96.4%. This method is effective in indicator diagram fault recognition of shock absorber.

Fault Diagnosis of Pump Based on a Hybrid HMM/SVM Model

2011 ◽  
Vol 188 ◽  
pp. 629-635
Author(s):  
Xia Yue ◽  
Chun Liang Zhang ◽  
Jian Li ◽  
H.Y. Zhu
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Model ◽  
Large Scale ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Double Layers ◽  
Support Vector ◽  
Practical Applications ◽  
Average Recognition Rate ◽  
Svm Model

A hybrid support vector machine (SVM) and hidden Markov model (HMM) model was introduced into the fault diagnosis of pump. This model had double layers: the first layer used HMM to classify preliminarily in order to get the coverage of possible faults; the second layer utilized this information to activate the corresponding SVMs for improving the recognition accuracy. The structure of this hybrid model was clear and feasible. Especially the model had the potential of large-scale multiclass application in fault diagnosis because of its good scalability. The recognition experiments of 26 statuses on the ZLH600-2 pump showed that the recognition capability of this model was sound in multiclass problems. The recognition rate of one bearing eccentricity increased from SVM’s 84.42% to 89.61% while the average recognition rate of hybrid model reached 95.05%. Although some goals while model constructed did not be fully realized, this model was still very good in practical applications.

scholarly journals An Appearance Invariant Gait Recognition Technique Using Dynamic Gait Features

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Hajra Masood ◽  
Humera Farooq
Keyword(s):  
South Asian ◽  
Cross Correlation ◽  
Gait Recognition ◽  
Gait Pattern ◽  
Visual Surveillance ◽  
Support Vector ◽  
Person Identification ◽  
Correlation Score ◽  
Gait Features ◽  
Dynamic Gait

Gait recognition-based person identification is an emerging trend in visual surveillance due to its uniqueness and adaptability to low-resolution video. Existing gait feature extraction techniques such as gait silhouette and Gait Energy Image rely on the human body’s shape. The shape of the human body varies according to the subject’s clothing and carrying conditions. The clothing choice changes every day and results in higher intraclass variance and lower interclass variance. Thus, gait verification and gait recognition are required for person identification. Moreover, clothing choices are highly influenced by the subject’s cultural background, and publicly available gait datasets lack the representation of South Asian Native clothing for gait recognition. We propose a Dynamic Gait Features extraction technique that preserves the spatiotemporal gait pattern with motion estimation. The Dynamic Gait Features under different Use Cases of clothing and carrying conditions are adaptable for gait verification and recognition. The Cross-Correlation score of Dynamic Gait Features resolves the problem of Gait verification. The standard deviation of Cross-Correlation Score lies in the range of 0.12 to 0.2 and reflects a strong correlation in Dynamic Gait Features of the same class. We achieved 98.5% accuracy on Support Vector Machine based gait recognition. Additionally, we develop a multiappearance-based gait dataset that captures the effects of South Asian Native Clothing (SACV-Gait dataset). We evaluated our work on CASIA-B, OUISIR-B, TUM-IITKGP, and SACV-Gait datasets and achieved an accuracy of 98%, 100%, 97.1%, and 98.8%, respectively.

scholarly journals Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

2012 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Nayan M. Kakoty ◽  
Mantoo Kaiborta ◽  
Shyamanta M. Hazarika
Keyword(s):  
Daily Living ◽  
Wavelet Decomposition ◽  
Recognition Rate ◽  
Support Vector ◽  
Robotic Hand ◽  
Radial Basis Function Kernel ◽  
Kernel Support Vector Machine ◽  
Average Recognition Rate ◽  
Electromyographic Signals

This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a five<br />fingered robotic hand to emulate six grasp types used during 70% daily living activities.<br /><br />

scholarly journals Detection of Mild Cognitive Impairment with MEG Functional Connectivity Using Wavelet-Based Neuromarkers

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6210
Author(s):  
Su Yang ◽  
Jose Miguel Sanchez Bornot ◽  
Ricardo Bruña Fernandez ◽  
Farzin Deravi ◽  
Sanaul Hoque ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cross Validation ◽  
Recognition Rate ◽  
Support Vector ◽  
Average Recognition Rate ◽  
Significant Performance ◽  
Healthy Control ◽  
Cortical Regions ◽  
Leave One Out

Studies on developing effective neuromarkers based on magnetoencephalographic (MEG) signals have been drawing increasing attention in the neuroscience community. This study explores the idea of using source-based magnitude-squared spectral coherence as a spatial indicator for effective regions of interest (ROIs) localization, subsequently discriminating the participants with mild cognitive impairment (MCI) from a group of age-matched healthy control (HC) elderly participants. We found that the cortical regions could be divided into two distinctive groups based on their coherence indices. Compared to HC, some ROIs showed increased connectivity (hyper-connected ROIs) for MCI participants, whereas the remaining ROIs demonstrated reduced connectivity (hypo-connected ROIs). Based on these findings, a series of wavelet-based source-level neuromarkers for MCI detection are proposed and explored, with respect to the two distinctive ROI groups. It was found that the neuromarkers extracted from the hyper-connected ROIs performed significantly better for MCI detection than those from the hypo-connected ROIs. The neuromarkers were classified using support vector machine (SVM) and k-NN classifiers and evaluated through Monte Carlo cross-validation. An average recognition rate of 93.83% was obtained using source-reconstructed signals from the hyper-connected ROI group. To better conform to clinical practice settings, a leave-one-out cross-validation (LOOCV) approach was also employed to ensure that the data for testing was from a participant that the classifier has never seen. Using LOOCV, we found the best average classification accuracy was reduced to 83.80% using the same set of neuromarkers obtained from the ROI group with functional hyper-connections. This performance surpassed the results reported using wavelet-based features by approximately 15%. Overall, our work suggests that (1) certain ROIs are particularly effective for MCI detection, especially when multi-resolution wavelet biomarkers are employed for such diagnosis; (2) there exists a significant performance difference in system evaluation between research-based experimental design and clinically accepted evaluation standards.

scholarly journals Motion Cue Analysis for Parkinsonian Gait Recognition

2013 ◽  
Vol 7 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Taha Khan ◽  
Jerker Westin ◽  
Mark Dougherty
Keyword(s):  
Gait Recognition ◽  
Recognition Rate ◽  
Gait Pattern ◽  
The Body ◽  
Motion Cues ◽  
Promising Tool ◽  
Marker Free ◽  
Normal Human ◽  
Leg Motion ◽  
Base Of Support

This paper presents a computer-vision based marker-free method for gait-impairment detection in Patients with Parkinson’s disease (PWP). The system is based upon the idea that a normal human body attains equilibrium during the gait by aligning the body posture with Axis-of-Gravity (AOG) using feet as the base of support. In contrast, PWP appear to be falling forward as they are less-able to align their body with AOG due to rigid muscular tone. A normal gait exhibits periodic stride-cycles with stride-angle around 45o between the legs, whereas PWP walk with shortened stride-angle with high variability between the stride-cycles. In order to analyze Parkinsonian-gait (PG), subjects were videotaped with several gait-cycles. The subject’s body was segmented using a color-segmentation method to form a silhouette. The silhouette was skeletonized for motion cues extraction. The motion cues analyzed were stride-cycles (based on the cyclic leg motion of skeleton) and posture lean (based on the angle between leaned torso of skeleton and AOG). Cosine similarity between an imaginary perfect gait pattern and the subject gait patterns produced 100% recognition rate of PG for 4 normal-controls and 3 PWP. Results suggested that the method is a promising tool to be used for PG assessment in home-environment.

scholarly journals Recognition of Handwritten English Numerals Based on Combining Structural and Statistical Features

2021 ◽  
pp. 73-83
Keyword(s):  
Recognition Rate ◽  
Structural Features ◽  
Features Extraction ◽  
Support Vector ◽  
Chain Code ◽  
Research Fields ◽  
Element Size ◽  
Average Recognition Rate ◽  
Intersection Points ◽  
Index Terms

Generally, pattern recognition considered a strong challenge in many information processing research fields. The aim of this paper is to propose a highly accurate model for recognizing a handwritten English numeral through efficiently extracting the most valuable features of a certain handwritten numeral or digit. The handwritten English Numerals Recognition Model (HENRM) is proposed in this paper. The features extraction of the proposal based on combining both statistical and structural features of the certain numeral sample image. Mainly, the proposed HENCM has four phases which are image acquisition, image preprocessing, features extraction, and classification. In fact, four feature extraction approaches are utilized in this paper, which are the number of intersection points, the number of open-end points, calculation of density feature, and determining the chain code for each of the English numerals. The latter phase gives a features vector of 26-element size to be fed into the classifier that uses the Multi-class Support Vector Machine (MSVM) for the classification process. The experimental results showed that the proposed HENCM exhibits an average recognition rate equals to 97%. Index Terms—Chain Code, Density feature, MSVM, Recognition.

HUMAN RECOGNITION BASED ON PLANTAR PRESSURE PATTERNS DURING GAIT

2013 ◽  
Vol 13 (02) ◽  
pp. 1350039
Author(s):  
YU-CHIH LIN ◽  
YU-TZU LIN
Keyword(s):  
Image Processing ◽  
Plantar Pressure ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Data Classification ◽  
Support Vector ◽  
Neuron Network ◽  
Recognition Method ◽  
Foot Pressure ◽  
Sensor Cell

Recognizing individuals by their gait is a new biometric methodology, which employs dynamic features derived from tracking gait. Instead of the image processing techniques used in most existing studies, our previous study initialized the work of investigating gait recognition in terms of biomechanics. The experimental results showed that the angles and forces of the lower limb joints were reliable features for recognition of individuals, which can provide us with a considerable amount of information in the field of computer science and thus help in developing a more efficient recognition method, which is also more computationally efficient than current image processing methods. Encouraged by the early results, in this study, we proposed a people recognition method based on plantar pressure patterns, which can be used in a concealed manner. We hoped to prove the feasibility of using foot pressure for individual recognition. Two different plantar pressure parameter measurement schemes are discussed: (1) the characteristic parameters and (2) the pressure values of each sensor cell in each frame. The self-organizing map (SOM) neuron network algorithm was used in both schemes for data classification. In order to improve the recognition rate, a support vector machine (SVM) was used as the data classification algorithm for the all-sensor-values method. High recognition rates were achieved with the second method, i.e., using all the sensor cell values of the foot pressure pattern during walking, regardless of the algorithm used. It is suggested that the foot pressure distribution of gait is a suitable feature for gait recognition. Both SOM and SVM can be feasible classifiers for foot pressure-based features.

Gait Recognition Algorithm Based on the Trajectory of Tiptoe

2011 ◽  
Vol 255-260 ◽  
pp. 1984-1988
Author(s):  
Yi Bo Li ◽  
Qin Yang
Keyword(s):  
Wavelet Packet ◽  
Gait Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Small Sample ◽  
Knee Joints ◽  
Support Vector ◽  
Gait Characteristics ◽  
Camera Angle ◽  
Academy Of Sciences

Most researchers focus on the gait characteristics of hip and changed angle of knee joints, gait characteristics of foot is still less attention, also apply wavelet packet to analysis more detailed information of characteristics’ data, and use the support vector machine algorithm to reduce the randomness, it has their unique advantages in the small sample. Summarized the above three points of the paper, the paper proposes a new gait recognition method to extract trajectory of tiptoe, uses wavelet packet to analyze it, then applies SVM for classification and recognition. Tested at the NLPR database of Chinese Academy of Sciences of 45 camera angle, we observed that the recognition rate has significantly increased, we observed that the algorithm is an effective identification method.

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