Hand gesture recognition of Arabic numbers using leap motion via deterministic learning

Hand gestures are spatio-temporal patterns which can be characterized by collections of spatio-temporal features. However, in real world scenarios, hand gesture recognition suffers from huge challenges with variations of illumination, poses and occlusions. The Microsoft Kinect device provides an effective way to solve the above issues and extract discriminative features for hand gesture recognition. The recognition approach consists of two stages: a training stage and a recognition stage. In the training stage, hand gesture features representing hand motion dynamics, including spatial position and direction of fingertips, are derived from Kinect. Hand motion dynamics underlying motion patterns of different gestures which represent Arabic numbers (0–9) are locally accurately modeled and approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated hand motion dynamics is stored in constant RBF networks. In the recognition stage, a bank of dynamical estimators is constructed for all the training patterns. Prior knowledge of hand motion dynamics represented by the constant RBF networks is embedded in the estimators. By comparing the set of estimators with a test gesture pattern to be recognized, a set of recognition errors are generated. The average L 1 norms of the errors are taken as the recognition measure between the dynamics of the training gesture patterns and the dynamics of the test gesture pattern according to the smallest error principle. By using the 2-fold and 10-fold cross-validation styles, the correct recognition rates are reported to be 95.83% and 97.25%, respectively.

This paper presents a new gait recognition method based on acceleration and angular velocity data captured by inertial sensors via deterministic learning. These gait features describe the motion trajectories of human gait and contain rich information for persons identification. The gait recognition approach consists of two phases: a training phase and a recognition phase. In the training phase, the gait dynamics underlying different individuals' gaits are represented by the acceleration and angular velocity features, and are locally accurately approximated by radial basis function (RBF) neural networks. The obtained knowledge of approximated gait dynamics is stored in constant RBF networks. In the recognition phase, a bank of dynamical estimators is constructed for all the training gait patterns. Prior knowledge of human gait dynamics represented by the constant RBF networks are embedded in the estimators. By comparing the set of estimators with a test gait pattern, a set of recognition errors are generated. The average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training gait patterns and the dynamics of the test gait pattern. The test gait pattern similar to one of the training gait patterns can be recognized according to the smallest error principle. Finally, comprehensive experiments are carried out on the OU-ISIR biometric gait database: inertial sensor dataset, which includes at most 744 subjects (389 males and 355 females) and is now the world's largest inertial sensor-based gait database, to demonstrate the recognition performance of the proposed algorithm.

Fault diagnosis for wireless sensor is very important to ensure signal acquisition precision. Radial basis function (RBF) neural network has strong classification ability. However, the selection of the connection weights, the hidden centers and the widths has an important influence on the classification performance of the RBF neural network in the learning process of RBF neural network. Thus, ant colony optimization is employ to gain the parameters of radial basis function neural network. Therefore, a novel method for fault diagnosis of wireless sensor based on RBF neural network and ant colony optimization is presented. The results of computational experiments show that ACO- RBF neural network has a great higher than RBF neural network.

For hand gesture recognition (HGR) sensing based on frequency modulated continuous wave (FMCW) radar, it is challenging to cope with the ambiguity and variability of gesture motions performed by different peoples due to the private habits or position, which inevitably reduce the robustness of HGR sensing. To solve this problem, we propose a novel dual-flow fusion deformable residual network (DFDRN). More detailed gesture information, time frequency maps and spectrum map videos, are fed into DFDRN, which improves the generalization and motion modeling ability of HGR through the extensive feature space. To further improve the robustness of HGR sensing, the deformable block is exploited to replace the convolution. To focus on the important features of dual-flow fusion, the convolutional block attention module (CBAM) is embedded in the backbone of DFDRN. Experimental results show that the accuracy of DFDRN, which can reach 97.5%, is obviously better than the state-of-the-art HGR methods.

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

Hand gesture recognition aims to recognize the meaningful expressions of hand motion. It is widely used in information visualization, robotics, sign language understanding, medicine and healthcare. Some methods have been proposed for hand gesture recognition. But no single algorithm can handle all kinds of situations, because of the complex environment. In this study, we propose a hybrid method for hand gesture recognition, which extends our previous work on a gesture recognition method based on concept learning by the addition of an association learning process. We use association learning to reveal the frequent patterns in gesture sequences, and then use such patterns to help recognize incomplete gesture sequences. Experiments show the use of association learning does indeed improve recognition accuracy. Experiments also show the hybrid method is comparable to two state of the art methods (HMMs and DTW) for hand gesture recognition, but outperforms them in the larger datasets.

In the process of moisture content's measurement with microwave resonant cavity based on RBF (radial basis function) neural network, the relationship among resonant frequency, quality factor and environmental temperature (the inputs of network) and moisture content (the output of network) is multiple non-linear. The performance of multiple non-linear regression algorithm is the major factor that determines the accuracy of measurement. A regression algorithm based on a RBF neural network is put forward to improve the measurement result in this paper. The RBF neural network regression algorithm can effectively avoid the BP algorithm disadvantages such as getting into local minimum and the low efficiency, and has the advantages such as high generalization precision and rapid convergence. Consequently, the measurement accuracy is enhanced. In this paper, experimental results show that the high-precision measurement value of moisture content can be obtained with the multiple non-linear regression algorithm based on RBF neural network.

Recently hand gesture recognition via millimeter-wave radar has attracted a lot of research attention for human-computer interaction. Encouraged by the ability of deep learning models in successfully tackling hand gesture recognition tasks, we propose a deep neural network (DNN) model namely, Res3DTENet that aims to classify dynamic hand gestures using the radio frequency (RF) signals. We propose a scheme that improves the convolutional process of 3DCNNs with residual skip connection (Res3D) to emphasize local-global information and enriches the intra-frame spatio-temporal feature representation. A multi-head attention transformer encoder (TE) network has been trained over the spatio-temporal features to refine the inter-frame temporal dependencies of range-Doppler sequences. The experiments are carried out on the publicly available Soli hand gesture data set. Based on our extensive experiments, we show that the proposed network achieves improved gesture recognition accuracy than the state-of-the-art hand gesture recognition methods.

Learning from adaptive neural network control of an underactuated rigid spacecraft

We propose in this work a dynamic group sparsity (DGS) based time-frequency feature extraction method for dynamic hand gesture recognition (HGR) using millimeter-wave radar sensors. Micro-Doppler signatures of hand gestures show both sparse and structured characteristics in time-frequency domain, but previous study only focus on sparsity. We firstly introduce the structured prior when modeling the micro-Doppler signatures in this work to further enhance the features of hand gestures. The time-frequency distributions of dynamic hand gestures are first modeled using a dynamic group sparse model. A DGS-Subspace Pursuit (DGS-SP) algorithm is then utilized to extract the corresponding features. Finally, the support vector machine (SVM) classifier is employed to realize the dynamic HGR based on the extracted group sparse micro-Doppler features. The experiment shows that the proposed method achieved 3.3% recognition accuracy improvement over the sparsity-based method and has a better recognition accuracy than CNN based method in small dataset.

In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error.

Dissolved oxygen (DO) as one of the most fundamental parameters of water quality plays a vital role in aquatic life. This study was conducted to predict DO, biological oxygen demand (BOD), and chemical oxygen demand (COD) in an intensive rainbow trout rearing system with different biomass (B). The multilayer perceptron (MLP) and the radial basis function (RBF) neural networks were employed for evaluating the impacts of food parameters (crude protein (CP), consumed feed (CF)), fish parameters (different values of B, and weight gain (WG)), and water quality parameters including temperature (T) and flow rate (Q) on variation of DO, BOD, and COD concentrations. This study's results showed that although both MLP and RBF neural networks are capable to estimate DO, BOD, and COD concentrations, RBF neural network showed better performance compared to MLP neural network. The results of sensitivity analysis indicated that the parameter CF has the highest effect on DO concentration estimation. Independent variables CF, CP, WG, and B showed the highest to the lowest rank of impacts on BOD estimation, respectively. The results also illustrated a decreasing trend of the effects on the estimation error of COD changes simulation by all independent variables, including B, T, WG, CF, CP, and Q, respectively. RBF neural network based on better stability and generalization ability with average root mean square error (RMSE) and mean absolute percentage error (MAPE) values of less than 0.12 and 3% was superior to MLP in DO, BOD, and COD concentration prediction. Moreover, CF was identified as the most effective factor in estima12tion process. Based on the present study results, there are direct relationships between DO, BOD, and COD concentrations and water quality parameters, fish parameters, and food parameters. Food parameters relative to fish and water quality parameters imposed the greatest effects. Improvement in feeding process such as application of intelligence feeding methods and change in fish diet and feeding time can considerably reduce losses in production system. Graphical abstract.

An adaptive feedback control scheme and a switching strategy for the output tracking stabilization of switched nonlinear systems with time-delay are presented by using RBF (radial basis function) neural network The RBF neural networks is used to compensate adaptively for the unknown nonlinear parts of switched nonlinear systems, and the approximation errors of RBF neural networks are introduced to the adaptive law in order to improve the tracking quality of the systems. The controller and switching strategy can guarantee asymptotic stability of tracking error for the overall system

This article introduces the design and implementation of a fishery forecasting system based on Radial Basis Function (RBF) neural network. The system was developed using the CLient/Server architecture, the C# programming language in the environment of Visual Studio 2008 on the Windows7 platform. It draws knowledge from RBF neural network theory, the production historical data of pelagic fishery and the marine environment data. The system uses the Object-Oriented analysis and design method. It can quickly obtain the forecast results available to users through inputting marine environment data information and the RBF neural network model. The forecasting system includes three major functional modules, namely preprocessing fishery production data, matching production data and environmental data, training RBF neural network and making predictions. Experiments have shown that this forecasting system can generate accurate and effective pelagic fishery knowledge.

To improve the accuracy of tracking unmanned vehicles on known trajectories, two optimised model predictive control (MPC) trajectory tracking control systems are designed based on the adaptive compensation and robust control of a radial basis function (RBF) neural network. Based on the traditional MPC trajectory tracking controller and the local approximation characteristics of the RBF neural network, the proposed RBF compensation–MPC control system is designed to compensate for the inaccuracy in the MPC prediction model arising from modelling errors. The results show that this method can achieve a root mean square error of less than 0.3703 m for the lateral position. Subsequently, to suppress the error generated by the RBF neural network and reduce the degree of vehicle sideslip, the error is considered to be external interference, and the anti-interference characteristic of the RBF robust control is incorporated into the RBF robust-MPC control system. Following the re-optimisation of the RBF robust control, the root mean square error of the lateral position is set within 0.2352 m. The results of a MATLAB/Carsim joint simulation show that using the RBF robust control can improve the tracking accuracy of the traditional MPC controller compared with RBF compensation control, while simultaneously improving the driving stability of the vehicle.