scholarly journals Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5466 ◽  
Author(s):  
Xinrui Jiang ◽  
Ye Zhang ◽  
Qi Yang ◽  
Bin Deng ◽  
Hongqiang Wang
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Millimeter Wave ◽  
Gait Recognition ◽  
Three Dimensional ◽  
Human Gait ◽  
Radar Signal ◽  
Learning Technology ◽  
Motion Speed ◽  
Signal Processing Algorithms

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

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.

scholarly journals Neural Networks Application for Processing of the Data from the FMICW Radars

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1308 ◽  
Author(s):  
Lubos Rejfek ◽  
Tan N. Nguyen ◽  
Pavel Chmelar ◽  
Ladislav Beran ◽  
Phuong T. Tran
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Processing ◽  
Continuous Wave ◽  
Doppler Radar ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Target Area ◽  
The Neural Network ◽  
Machine Learning Applications

In this paper the results of the Neural Networks and machine learning applications for radar signal processing are presented. The radar output from the primary radar signal processing is represented as a 2D image composed from echoes of the targets and noise background. The Frequency Modulated Interrupted Continuous Wave (FMICW) radar PCDR35 (Portable Cloud Doppler Radar at the frequency 35.4 GHz) was used. Presently, the processing is realized via a National Instruments industrial computer. The neural network of the proposed system is using four or five (optional for the user) signal processing steps. These steps are 2D spectrum filtration, thresholding, unification of the target, target area transforming to the rectangular shape (optional step), and target board line detection. The proposed neural network was tested with sets of four cases (100 tests for every case). This neural network provides image processing of the 2D spectrum. The results obtained from this new system are much better than the results of our previous algorithm.

A multilevel paradigm for deep convolutional neural network features selection with an application to human gait recognition

2020 ◽  
Author(s):  
Habiba Arshad ◽  
Muhammad Attique Khan ◽  
Muhammad Irfan Sharif ◽  
Mussarat Yasmin ◽  
João Manuel R. S. Tavares ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Gait Recognition ◽  
Deep Convolutional Neural Network ◽  
Human Gait ◽  
Features Selection

scholarly journals A Determination Method for Gait Event Based on Acceleration Sensors

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5499 ◽  
Author(s):  
Chang Mei ◽  
Farong Gao ◽  
Ying Li
Keyword(s):  
Motion Capture ◽  
Inertial Sensors ◽  
Gait Recognition ◽  
Three Dimensional ◽  
High Standard ◽  
Vertical Displacement ◽  
Human Gait ◽  
Vertical Acceleration ◽  
Acceleration Signal ◽  
Acceleration Sensors

A gait event is a crucial step towards the effective assessment and rehabilitation of motor dysfunctions. However, for the data acquisition of a three-dimensional motion capture (3D Mo-Cap) system, the high cost of setups, such as the high standard laboratory environment, limits widespread clinical application. Inertial sensors are increasingly being used to recognize and classify physical activities in a variety of applications. Inertial sensors are now sufficiently small in size and light in weight to be part of a body sensor network for the collection of human gait data. The acceleration signal has found important applications in human gait recognition. In this paper, using the experimental data from the heel and toe, first the wavelet method was used to remove noise from the acceleration signal, then, based on the threshold of comprehensive change rate of the acceleration signal, the signal was primarily segmented. Subsequently, the vertical acceleration signals, from heel and toe, were integrated twice, to compute their respective vertical displacement. Four gait events were determined in the segmented signal, based on the characteristics of the vertical displacement of heel and toe. The results indicated that the gait events were consistent with the synchronous record of the motion capture system. The method has achieved gait event subdivision, while it has also ensured the accuracy of the defined gait events. The work acts as a valuable reference, to further study gait recognition.

scholarly journals Gait Recognition Analysis for Human Identification Analysis- A Hybrid Deep Learning Process

2021 ◽  
Author(s):  
Mathivanan B ◽  
Perumal P
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Median Filter ◽  
Gait Recognition ◽  
Human Gait ◽  
Scale Invariant ◽  
Inference System ◽  
Statistical Measures ◽  
Hu Moments

Abstract Gait is an individual biometric behavior which can be detected based on distance which has different submissions in social security, forensic detection and crime prevention. Hence, in this paper, Advanced Deep Belief Neural Network with Black Widow Optimization (ADBNN-BWO) Algorithm is developed to identify the human emotions by human walking style images. This proposed methodology is working based on four stages like pre-processing, feature extraction, feature selection and classification. For the pre-processing, contrast enhancement median filter is used and Hu Moments, GLCM, Fast Scale-invariant feature transform (F-SIFT), in addition skeleton features are used for the feature extraction. To extract the features efficiently, the feature extraction algorithm can be often very essential calculation. After that, feature selection is performed. Then the classification process is done by utilizing the proposed ADBNN-BWO Algorithm. Based on the proposed method, the human gait recognition is achieved which utilized to identify the emotions from the walking style. The proposed method is validated by using the open source gait databases. The proposed method is implemented in MATLAB platform and their corresponding performances/outputs are evaluated. Moreover, the statistical measures of proposed method are also determined and compared with the existing method as Artificial Neural Network (ANN), Mayfly algorithm with Particle Swarm Optimization (MA-PSO), Recurrent Neural Network -PSO (RNN-PSO) and Adaptive Neuro Fuzzy Inference System (ANFIS) respectively.

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