Robust gait recognition: a comprehensive survey

Rida, Imad; Almaadeed, Noor; Al‐Maadeed, Somaya

doi:10.1049/iet-bmt.2018.5063

Cited by 106 publications

(59 citation statements)

References 200 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, in terms of convergence time, the two methods in [14,15] did not consider the parallel computing problem, so the convergence time of the algorithm increased exponentially with the increase in the number of parallel threads. In the meantime, since the algorithm in [16,17] takes account of the compression problem in the setting stage, its calculation time is slightly better than the algorithm in [18,19]. In addition, the algorithm in this paper considers the problem of parallel execution and uses a quad-core processor, so there is no obvious change in the computing time, thus showing the higher computational efficiency of the algorithm [20,21].…”

Section: Total Time 3hourmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

View full text Add to dashboard Cite

Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

show abstract

Section: Total Time 3hourmentioning

confidence: 99%

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Wang

2020

Symmetry

View full text Add to dashboard Cite

show abstract

“…The number of floating numbers N kNN used by a trained kNN can be approximated using (13). kNN requires the training data to be stored on the device so that the distance between new features and the training data can be calculated for classification.…”

Section: Memory Requirementmentioning

confidence: 99%

“…Neural network [3][4][5], k-nearest neighbour (kNN) [6,7], support vector machine (SVM) [8,9], and clustering method [10] are some common machine learning techniques used for gait classification. Other than being useful for medical diagnosis [11], gait classification applies for activity recognition [12] and biometric identification [13].…”

Section: Introductionmentioning

confidence: 99%

Comparison of machine learning methods for the construction of a standalone gait diagnosis device

Han¹,

Wong²,

Murray

2020

IET signal process.

View full text Add to dashboard Cite

In this research, the authors investigate the feasibility of selecting three-dimensional thigh and shank angles as the features of machine learning methods. Four common machine learning techniques, i.e. random forest, k-nearest neighbour, support vector machine and perceptron, were compared in terms of accuracy and memory usage so that a real-time standalone gait diagnosis device can be constructed using low-end inertial measurement units (IMUs). With proper re-sampling and normalisation, they discovered that the support vector machine and perceptron resulted in the top two highest accuracies (96-99%) among the four machine learning methods. The memory requirement of the perceptron is the lowest among the machine learning methods. Therefore, perceptron was selected as the classification algorithm for the standalone gait diagnosis device. The trained perceptron was transferred to the thigh and shank's IMUs to process the data locally in real-time. The constructed standalone gait diagnosis device lit up green or red light emitting diodes when normal or abnormal gaits were detected, respectively. This standalone device was further tested in real-life and achieved a mean classification accuracy of 96.50%. AccGy stands for acceleration and gyroscope. The extracted features are similar to that of Mikos et al. [4]. DFT stands for discrete Fourier transform. The features are the first 5 harmonic components (5 instantaneous amplitudes and 5 phases) of the angle waveform. These results were obtained using all features extracted from 3D thigh and shank sensor data. Values in bold font means ≥ 96%.

show abstract

“…In the case of physical biometrics, authentication has been conducted using direct measurements of a part of human body, such as fingerprint,[ 2 ] face,[ 3 ] and iris. [ 4 ] On the other hand, behavioral biometrics use the information of an action performed by the user such as voice,[ 5 ] gait,[ 6 ] and signature. [ 7 ] Biometric-based authentication systems use many different aspects of human physiology, chemistry, or behavior.…”

Section: Introductionmentioning

confidence: 99%

Online handwritten signature verification and recognition based on dual-tree complex wavelet packet transform

2020

View full text Add to dashboard Cite

Robust gait recognition: a comprehensive survey

Cited by 106 publications

References 200 publications

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Comparison of machine learning methods for the construction of a standalone gait diagnosis device

Online handwritten signature verification and recognition based on dual-tree complex wavelet packet transform

Contact Info

Product

Resources

About