Using the Kalman Filter for Auto Bit-rate H.264 Streaming Based on Human Interaction

In safety monitoring of slope, a collecting apparatus was inconvenient in operation, low in efficiency, restricted in application. Based on analysis of technical requirements about safety monitoring of slope, the design thinking of human computer interface for the intellective collecting apparatus of slope monitoring was put forward, human machine interface WEINVIEW TK8070IH suitable for slope monitoring was chosen, the software of the human machine interface was developed based on a software EasyBuilder, the collecting apparatus of slope monitoring with human machine interaction was developed with multi-function, remote, intelligent, simple, indoor test and in-field application of the apparatus were carried out. Test and application showed, sensors accessed to the intellective collecting apparatus did not rely on in-field computer operation, with the collecting apparatus, we could set/modify the parameters of sensors, dynamically adjust the parameter of collecting interval, copy data, view the results and post-process data and so on, it could collect and transfer data for remote distance, it was efficient and convenient. The collecting apparatus can be applied in safety monitoring of slope projects for civil, water conservancy, traffic, railway etc.

Recently there is an increasing interest in video based interface techniques, allowing more natural interaction between users and systems than common interface devices do. We present a neural architecture for user localisation, embedded within a complex system for visually-based human machine interaction (HMI). User localisation is an absolute prerequisite to video based HMI. Due to the main objective, the greatest possible robustness of the localisation as well as the whole visual interface under highly varying environmental conditions, we propose a multiple cue approach. This approach combines the features of facial structure, head shoulder contour, skin color and motion, with a multiscale representation. The selection of the image region most likely containing a possible user is then realised via a WTA-process within the multiscale representation. Preliminary results show the reliability of the multiple cue approach.

Human-computer interaction for virtual-real fusion

With the development of high-speed and high-precision printing, the shaft-less drive system has been widely used in roll-to-roll gravure printing machines. In order to meet the functional demand of controlling printing unit conveniently and quickly, this paper designed an embedded human-machine interface (HMI) system of printing unit for the gravure printing machine. Based on analyzing the structure and composition of the shaft-less drive system, the functional requirements of the printing unit HMI system are analyzed, and the functional scheme of the embedded HMI system is proposed. Then the hardware circuit of the HMI system is designed with ARM as the core, and the software of the HMI system is developed with Keil C. Finally, the performance of the HMI system is studied by experiment. The experimental results show that the embedded HMI system can meet the requirements of printing unit control operation of roll-to-roll gravure printing machine.

Conventional human-machine interface (HMI) systems generally take surface electromyography as the only signal source, which often faces the problem of multi-channel crosstalk. To enhance HMI performance, this study introduces a novel three-degree-of-freedom HMI system for surgical robot control, which integrates surface electromyography signals and inertial measurement unit signals. A cost-effective wearable HMI hardware system is designed to address the high price and poor portability of existing commercial equipment, which also has a higher signal-to-noise ratio of 35.9 dB. Based on the proposed simultaneous and proportional control strategy, ten subjects were recruited to conduct an online experiment involving four upper limb postures to assess the real-time performance of the HMI system. Additionally, six able-bodied participants participated in a validation experiment to statistically verify and rigorously evaluate the implemented HMI system. The experimental results demonstrate that our HMI system can accurately decode the angles of the wrist and metacarpophalangeal joints in both single-degree-of-freedom and three-degree-of-freedom movements, achieving stable control and robust online performance. Furthermore, the proposed HMI system was applied to a flexible endoscopic robot to realize remote simultaneous and proportional control, and the results indicate that it can perform well in the practical application. The current study proves the feasibility and practicability of multi-source data fusion for HMI, providing a new direction.

Advanced technologies such as robotics, 5G mobile communications, IoT, cloud computing and wireless sensor networks have had a huge impact and influence on manufacturing, with an increased collaboration between humans and smart systems. As the manufacturing process becomes more automated using real-time data, communication systems, Artificial Intelligence (AI) techniques and robotics feed data back into the manufacturing process. This enables the design of products that are more customized and personal, and leads to a more competitive, efficient and value-added production process by reacting more quickly to technical or human errors to avoid product and system damage while increasing workplace safety, and reducing waste, pollution, and associated costs. This edited book covers challenges, concepts, systems, architectures, technologies, and design characteristics of human-machine cooperation and interaction systems in smart manufacturing environments using state of the art technologies including AI, 5G, IoTs, Blockchains, CPS, sensing, automation and robotics. The book is aimed at researchers and engineers working on the applications of robotics and automation, HMI, HCI, CPS, sensing, information and communications technology, data science, ML/DL/AI, AR/VR, cybersecurity and electronics. It is also a useful reference for advanced students and lecturers in these fields, and will appeal to manufacturers and automation system developers.

Stretchable strain sensors play a vital role in monitoring human body movements and have been widely applied in human–machine interface (HMI) systems. Conventional planarfabricated sensors attached to curved surfaces often suffer from low fabrication efficiency and reduced measurement accuracy due to poor conformability. This study presents a laser direct writing–based approach for the direct fabrication of double‐layer strain sensor arrays, interconnects and electrodes on complex curved, stretchable substrates. The resulting strain sensor exhibits a high gauge factor of 10⁶ at 80% strain and a minimum detectable strain as low as 0.1%, enabling precise monitoring of joint motion across a bending range of 0° to 90°. Additionally, tuning the material composition and structural design enables precise control over the sensor's electromechanical responses to suit various HMI applications. As a demonstration, an HMI with integrated strain sensors and interconnect arrays is directly fabricated onto the 3D surfaces of a glove. This system enables the robotic hand to accurately mimic multiple human gestures, highlighting its potential for advanced human–machine interaction.

Traditionally, human machine interactions (HMI) occur in the form of simple, typically single flows of uniform events, such as mouse clicks on Graphical User Interface (GUI). Researchers in the HMI field aim to improve the HMI experience using a more natural approach. Interaction infrastructure is becoming more pervasive, and terminal devices are increasingly equipped with multiple sensors, such as video cameras or audio/video equipment, capable of collecting information from the environment. Video sensors are already placed in nearly every kind of device with which humans interact, such as PCs, mobile phones, and PDAs. In this paper, we propose that in Web 2.0-3.0, the interactive experience could be greatly improved from the client point of view with the use of a pervasive computing paradigm. We focus our attention on improvements that can be obtained with video sensors, and describe a more general architecture that is compliant with the current web infrastructure.

Classification of hand gesture is crucial for the development of hand gesture based system for human machine interaction. Gesture recognition system consists of hand gesture acquisition, segmentation, morphological filtering, contour extraction, and classification. This paper aims at classification of hand gesture as a similarity measure using Dynamic Time Warping and Piecewise Dynamic Time Warping. Experiments and evaluation on a subset of American Sign Language (ASL) hand gesture show that, by using Dynamic Time Warping hand gesture can be classified. Additionally, it is also estimated that Piecewise DTW can be efficiently used to speed up the computations of DTW.

In this study, a head-mounted camera was used to track eye behaviors and estimate the gaze point on the user’s visual plane. The integration of the elastic mechanism design makes the headset adaptable for various users. The wearable cases were prototyped with low-cost cameras to produce an efficient eye tracking solution. This proposed system can effectively extract and estimate pupil ellipse from a few camera images of an eye and compute the corresponding three-dimensional eye model. The system can match later images of the same pupil ellipse from a head-mounted camera to give the possible visual angles. To estimate the gaze point, the system uses multiple-point calibration to solve the related polynomial formula for future angle-to-gaze mapping. The proposed eye-tracking algorithms can provide a low-complexity solution with high accuracy, precision, and speed. This tracking system is a low-cost and promising system that can be used in headsets for virtual reality, auxiliary equipment, interactive machine, and human–machine interface applications. The proposed eye-tracking algorithm can achieve satisfactory performance without using a high-end high-speed camera and can be detected under different lighting sources, and the average errors of the detection results are stably within 9 pixels and at a distance of 50 cm from the screen; while the average error of the fixation mapping results is within 3°.

Antifreeze protein-based ultrastretchable and highly sensitive hydrogel for human-machine interaction

Cooperative Markov Decision Process model for human–machine co-adaptation in robot-assisted rehabilitation

BackgroundRecently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.MethodsIn this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network.ResultsThe average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy. Moreover, the results proved a very fast procedure since the training time during classification via VEBFNN was 0.105 seconds. It was also indicated that MRMR was not a proper criterion to be used for making more effective feature sets in comparison with RA.ConclusionsThis work was accomplished by introducing the most discriminating facial EMG time-domain feature for the recognition of different facial gestures; and suggesting VEBFNN as a promising method in EMG-based facial gesture classification to be used for designing interfaces in human machine interaction systems.

Today's manufacturing and building operations are faced with the need to reduce cost and be more competitive with the fewest of resources. Connectivity to different infrastructures for data gathering and the need to analyze and visualize data in real time is ever more important in today's economic environment. Recently, visualization systems have taken a giant step forward incorporating advanced hardware accelerated 3D graphics into standard off-the commercial product. As a result, providing compelling 3D graphics applications and having access to plant data is the key to having a competitive advantage. This paper presents a whole new powerful approach to visualization of manufacturing that greatly reduces the learning curve for developing Human Machine Interface (HMI) applications.

Human detection and tracking is an interesting field of research in computer vision and image processing areas. It is widely used in video surveillance, robotics, human machine interaction and other applications. The automated object detection and tracking is still a challenging task that needs to be addressed. Hence the main idea is to develop a system based on various image processing techniques to reliably detect and track people in video sequence from stationary cameras. The proposed method can be viewed as consisting of two stages namely detection and tracking. In detection stage, Histogram of Oriented Gradients popularly known as HOG is used as a feature descriptor. HOG features are robust to local changes in geometry and illumination but it is computationally expensive. This disadvantage of HOG is due to its exhaustive scanning approach over entire region of interest. The main contribution of this paper is to reduce the computational time of HOG by dynamically determining the region of interest and limiting the scan area. Further Principal component analysis is used to reduce the dimensionality of HOG features. The additional use of optical flow based tracking eliminates the need of HOG computation in every frame. Hence the person detected by HOG in initial frame is successfully tracked in subsequent frames and reduces HOG computation time. The proposed dynamic ROI selection method not only reduces detection time but also reduces the number of false positives and increases the efficiency. The experimental results show that the system efficiently detects and tracks people in videos without much of occlusion.