EMG-based human-machine interface system

According to the properties of the CNC precision internal grinding, the Human-machine interface system for the CNC based on industrial-grade touch device is proposed in this paper. Based on analyzing the human-machine interface system on the whole, each module is described in detail. The whole human-machine interface system is friendly, convenient and flexible, which meet the function requirements of embedded CNC precision internal grinder.

In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).

Nowadays, the research on wearable electronics have received tremendous attraction because of their potential applications in personalized health monitoring and treatment, energy conversion and storage, and human-machine interface system. Herein, we report a facile route for the fabrication of electrically conductive cotton fabric with excellent joule heating and high electromagnetic shielding performances using graphene oxide (GO) and silver nitrate (AgNO3). The GO used in this study is exclusively synthesized from spent batteries in order to minimize the environmental pollution. The surface morphology, elemental analysis, electrical conductivity, thermo-heating behavior and electromagnetic shielding performance have been studied systematically. Due to the high electrical conductivity, the GO-Ag coated cotton with 5 wt% of GO reached high surface temperature of 117.8°C within 35 s, and also it exhibits high electromagnetic interference shielding efficiency value of 79.08 dB. The high flexibility, excellent conductivity, electromagnetic shielding efficiency and joule heating performance of GO-Ag coated cotton fabric suggesting that the GO synthesized from spent batteries will be a potential and valuable resource for the new generation of wearable electronics.

High-performance flexible pressure sensors are highly desirable in health monitoring, robotic tactile, and artificial intelligence. Construction of microstructures in dielectrics and electrodes is the dominating approach to improving the performance of capacitive pressure sensors. Herein, we have demonstrated a novel three-dimensional microconformal graphene electrode for ultrasensitive and tunable flexible capacitive pressure sensors. Because the fabrication process is controllable, the morphologies of the graphene that is perfectly conformal with the electrode are controllable consequently. Multiscale morphologies ranging from a few nanometers to hundreds of nanometers, even to tens of micrometers, have been systematically investigated, and the high-performance capacitive pressure sensor with high sensitivity (3.19 kPa-1), fast response (30 ms), ultralow detection limit (1 mg), tunable-sensitivity, high flexibility, and high stability was obtained. Furthermore, an ultrasensitivity of 7.68 kPa-1 was successfully achieved via symmetric double microconformal graphene electrodes. The finite element analysis indicates that the microstructured graphene electrode can enhance large deformation and thus effectively improve the sensitivity. Additionally, the proposed pressure sensors are demonstrated with practical applications including insect crawling detection, wearable health monitoring, and force feedback of robot tactile sensing with a sensor array. The microconformal graphene may provide a new approach to fabricating controllable microstructured electrodes to enhance the performance of capacitive pressure sensors and has great potential for innovative applications in wearable health-monitoring devices, robot tactile systems, and human-machine interface systems.

Accurate temperature monitoring in the furnace is an important element in the processing of steel metals at PT Krakatau Baja Construction. Type S thermocouple sensors are used to measure temperature by converting temperature changes into voltage signals. However, to ensure the accuracy of the readings, thermocouple performance verification is carried out periodically. This research aims to verify thermocouples in several furnace zones to increase the accuracy of temperature data in the Human Machine Interface (HMI) system. The verification process uses the Fluke 17 B+ tool and Universal Transmitter PR4116. The measurement results are compared with the reference table and HMI readings before and after verification. The results show that all thermocouples function well with the largest deviation of 23°C in Zone 2A before adjustment and the smallest deviation of 1°C in Zone 1. Verification succeeded in reducing the deviation, ensuring the reliability of the measurement system. This research confirms the importance of thermocouple verification to improve production efficiency and product quality, with recommendations for regular evaluation of the HMI system and temperature measurements.

In human visual perception, there is an intuitive tolerance (perceptual constancy) for perceiving raw stimulus patterns which may be rotated, scaled, deformed, or noisy to some extent for a learned pattern. Such an intuitive property of perception is a major feature of human pattern recognition, where a recognition mechanism (consider the character recognition for example) may not necessarily follow in a stroke‐based (feature‐oriented) scheme; rather, it may follow in a whole‐image‐based (spatial‐oriented) scheme. In this paper, we present a conceptual development framework for intuitive human pattern recognition. The implementation model is called I‐net and basically consists of three parts: an attention mechanism, a generalization mechanism, and a recognition mechanism. The paper begins with describing the intuitive properties involved in recognizing 2‐D binary patterns, following an introduction to intuitive human pattern recognition. Then the details of the I‐net are described and experimental results are presented. The paper concludes that the proposed framework provides another direction for approaching human pattern recognition.

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.

Within the framework of pattern recognition, many methods of classification were developed. More recently, techniques using the Dempster-Shafer's theory or evidence theory tried to deal with the problem related to the management of the uncertainty and data fusion. We propose a classification method based on Dempster-Shafer's theory and information criteria. After an original basic belief assignment method, we introduce an attenuation factor of belief functions based on the dissimilarity between probability distributions. Results on synthetic and real world data sets are given in order to illustrate the proposed methodology.

In this paper, we introduce a new model of solving pattern recognition tasks called PRISM (Pattern Recognition using Information Slicing Method). The main concept behind PRISM is the slicing of information through multiple planes across different feature axes to generate a number of cells. The number of cells created and their volume depends upon the number partitions per axes. In this context we define resolution as the number of partitions per axes. In this paper, we make the following contributions. First, we provide a brief survey of the class separability measures and feature partitioning schemes used for pattern recognition. Secondly, we define the PRISM framework and the algorithm for data assignment to cells. Thirdly, we detail four important concepts in PRISM: purity, neighbourhood separability, collective entropy, and data compactness. The first two measures define the data complexity, the next measure relates to uncertainty, and the last measure defines the alternative to statistical data variance in the PRISM framework. Fourthly, we investigate the variability in the estimates of these measures depending on the placement of partitions on each feature axis. Finally, we give an overview of experimental successes achieved with PRISM in the areas of classification complexity estimation and feature selection.

The dexterity and coordinated movement of the hand play a very important role in people’s daily life and interpersonal communication. For patients with hand amputation, there are many inconveniences in life. However, prosthetic limb can help them to carry out daily life in accordance with their own intention to meet the basic grasp and interpersonal gesture needs of daily life, which can greatly improve the quality of life of patients. The bionic manipulator is such a humanoid prosthetic limb for the rehabilitation of amputees and is controlled by the surface electromyography (sEMG) signals on the surface of the human body. In this paper, a wearable and portable gesture recognition system based on pattern recognition algorithm is established to control the bionic manipulator in real time. We enhanced the accuracy in each step of signal acquisition and processing. By analyzing the mechanism and characteristics of sEMG signals, we determined the gestures to be identified, the areas of muscle, the number of electrodes, and the placement positions. Our self-developed acquisition device was used to acquire 4-channel forearm sEMG signals and data preprocessing, window sliding, feature extraction, gesture classification were conducted.

Remote welding robot system may be used for nuclear components maintenance, space assemble, underwater structures repair. This paper describes the remote welding robot system (RWRS) based on shared control. The paper introduces the structure of remote welding robot system, stereo vision system, autonomous seam-tracking system, the human-machine interface and graphics simulation system of teleoperation.

Automatic media conversion schemes from speech to a facial image and a construction of a real-time image synthesis system are presented. The purpose of this research is to realize an intelligent human-machine interface or intelligent communication system with synthesized human face images. A human face image is reconstructed on the display of a terminal using a 3-D surface model and texture mapping technique. Facial motion images are synthesized by transformation of the 3-D model. In the motion driving method, based on vector quantization and the neural network, the synthesized head image can appear to speak some given words and phrases naturally, in synchronization with voice signals from a speaker. >

Gesture recognition framework for upper-limb prosthetics using entropy features from electromyographic signals and a Gaussian kernel SVM classifier

A proposed framework for control chart pattern recognition in multivariate process using artificial neural networks

Surface electromyography (sEMG) signals have been applied as control commands in numerous human-robot interface systems and have been deployed for rehabilitation or clinical applications. Although lots of previous workers have tried to determine features appropriate for specific sEMG-signal classification problems, little of this work has involved deeply searching for the inner characteristics of the signals. In this study, we try to evaluate the properties of the transient state of sEMG signals on randomly mounted, dry-type electrodes and use this to rapidly predict three kinds of hand configurations - rock, scissors and paper motions. In experiments, subjects performed a rock-scissor-paper game with a virtual hand. For data acquisition, the sEMG signals were sampled at 1 kHz with eight-channel electrodes (wearable, dry type) that were randomly mounted on forearms [2]. The results verified that the proposed algorithm, using the property of the transient state of sEMG signals, works successfully.