Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

Intelligent human computer interaction based on non redundant EMG signal

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

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Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.

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Hand gesture classification using a novel CNN-crow search algorithm

In order to ensure stable gripping performance of manipulator in a dynamic environment, a target object grab setting model based on the candidate region suggestion network is established with the multi-target object and the anchor frame generation measurement strategy overcoming external environmental interference factors such as mutual interference between objects and changes in illumination. In which, the success rate of model detection is improved by adding small-scale anchor values for small area grabbing target position detection. Further, 94.3% crawl detection success rate is achieved on the multi-target detection data sets using the information fusion of color image and depth image. The methods in this paper effectively improve the model’s robustness and crawl success rate.

Manipulator grabbing position detection with information fusion of color image and depth image using deep learning

Feature extraction is one of most important steps in the control of multifunctional prosthesis based on surface electromyography (sEMG) pattern recognition. In this paper, a new sEMG feature extraction method based on muscle active region is proposed. This paper designs an experiment to classify four hand motions using different features. This experiment is used to prove that new features have better classification performance. The experimental results show that the new feature, active muscle regions (AMR), has better classification performance than other traditional features, mean absolute value (MAV), waveform length (WL), zero crossing (ZC) and slope sign changes (SSC). The average classification errors of AMR, MAV, WL, ZC and SSC are 13%, 19%, 26%, 24% and 22%, respectively. The new EMG features are based on the mapping relationship between hand movements and forearm active muscle regions. This mapping relationship has been confirmed in medicine. We obtain the active muscle regions data from the original EMG signal by the new feature extraction algorithm. The results obtained from this algorithm can well represent hand motions. On the other hand, the new feature vector size is much smaller than other features. The new feature can narrow the computational cost. This proves that the AMR can improve sEMG pattern recognition accuracy rate.

A novel feature extraction method for machine learning based on surface electromyography from healthy brain

The principal component analysis method and GRNN neural network are used to construct the gesture recognition system, so as to reduce the redundant information of EMG signals, reduce the signal dimension, improve the recognition efficiency and accuracy, and enhance the feasibility of real-time recognition. Using the means of extracting key information of human motion, the specific action mode is identified. In this paper, nine static gestures are taken as samples, and the surface EMG signal of the arm is collected by the electromyography instrument to extract four kinds of characteristics of the signal. After dimension reduction and neural network learning, the overall recognition rate of the system reached 95.1%, and the average recognition time was 0.19 s.

Surface EMG hand gesture recognition system based on PCA and GRNN

With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal.

Grasping posture of humanoid manipulator based on target shape analysis and force closure

The channel numbers and electrode layouts are usually determined empirically that would reduce robustness when acquiring surface electromyography (EMG) signals for prosthetic hand systems. It is ne...

An Optimized Selection Method of Channel Numbers and Electrode Layouts for Hand Motion Recognition

In order to solve the problem of uneven distribution of picture features and stitching of images, an improved SURF feature extraction method is proposed. Image feature extraction and image registration are the core of image stitching, which is directly related to stitching quality. In this paper, a comprehensive and in-depth study of feature-based image registration is carried out, and an improved algorithm is proposed. Firstly, the Heisen detection operator in the SURF algorithm is introduced to realize feature detection, and the features are extracted as much as possible. Secondly, the characteristics are described by BRIEF operator in the ORB algorithm to realize the invariance of the rotation change. Then, the European pull distance is used to complete the similarity calculation, and the KNN algorithm is used to realize the feature rough matching. Finally, the distance threshold is used to remove the matching pair with larger distance, and then the RANSAC algorithm is used to complete the purification. Experiments show that the proposed algorithm has good real-time performance, strong robustness and high accuracy.

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Jinxian Qi

Papers

Surface EMG hand gesture recognition system based on PCA and GRNN

Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

Grasping posture of humanoid manipulator based on target shape analysis and force closure

An Optimized Selection Method of Channel Numbers and Electrode Layouts for Hand Motion Recognition

Image stitching based on improved SURF algorithm