scholarly journals High‐performance optical noncontact controlling system based on broadband PtTe x /Si heterojunction photodetectors for human–machine interaction

InfoMat ◽  
2021 ◽  
Author(s):  
Zhexin Li ◽  
Wenhao Ran ◽  
Yongxu Yan ◽  
Linlin Li ◽  
Zheng Lou ◽  
...  
Keyword(s):  
High Performance ◽  
Human Machine Interaction ◽  
Controlling System ◽  
Machine Interaction

scholarly journals Electromechanical Design of Self-Similar Inspired Surface Electrodes for Human-Machine Interaction

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
YongAn Huang ◽  
Wentao Dong ◽  
Chen Zhu ◽  
Lin Xiao
Keyword(s):  
High Performance ◽  
Electrical Coupling ◽  
Interfacial Strength ◽  
Motion Artifacts ◽  
High Signal ◽  
Electrical Stability ◽  
Human Machine Interaction ◽  
Surface Electrodes ◽  
Self Similar ◽  
Machine Interaction

Stable acquisition of electromyography (EMG)/electrocardiograph (ECG) signal is critical and challenging in dynamic human-machine interaction. Here, self-similar inspired configuration is presented to design surface electrodes with high mechanical adaptability (stretchability and conformability with skin) and electrical sensitivity/stability which are usually a pair of paradoxes. Mechanical and electrical coupling optimization strategies are proposed to optimize the surface electrodes with the 2nd-order self-similar serpentine configuration. It is devoted the relationship between the geometric shape parameters (height-space ratio η, scale factor β, and line width w), the areal coverage α, and mechanical adaptability, based on which an open network-shaped electrode is designed to stably collect high signal-to-noise ratio signals. The theoretical and experimental results show that the electrodes can be stretched > 30% and conform with skin wrinkle. The interfacial strength of electrode and skin is measured by homemade peeling test experiment platform. The surface electrodes with different line widths are used to record ECG signals for validating the electrical stability. Conformability reduces background noises and motion artifacts which provides stable recording of ECG/EMG signals. Further, the thin, stretchable electrodes are mounted on the human epidermis for continuous, stable biopotential signal records which suggests the way to high-performance electrodes in human-machine interaction.

scholarly journals Control of Robot Directions Based on Online Hand Gestures

2018 ◽  
Vol 14 (1) ◽  
pp. 41-50
Author(s):  
Mohammed Tawfeeq ◽  
Ayam Abbass
Keyword(s):  
Communication Technology ◽  
High Performance ◽  
Robotic Systems ◽  
Wireless System ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Wheeled Robot ◽  
Short Time ◽  
Machine Interaction ◽  
Very High

The evolution of wireless communication technology increases human machine interaction capabilities especially in controlling robotic systems. This paper introduces an effective wireless system in controlling the directions of a wheeled robot based on online hand gestures. The hand gesture images are captured and processed to be recognized and classified using neural network (NN). The NN is trained using extracted features to distinguish five different gestures; accordingly it produces five different signals. These signals are transmitted to control the directions of the cited robot. The main contribution of this paper is, the technique used to recognize hand gestures is required only two features, these features can be extracted in very short time using quite easy methodology, and this makes the proposed technique so suitable for online interaction. In this methodology, the preprocessed image is partitioned column-wise into two half segments; from each half one feature is extracted. This feature represents the ratio of white to black pixels of the segment histogram. The NN showed very high accuracy in recognizing all of the proposed gesture classes. The NN output signals are transmitted to the robot microcontroller wirelessly using Bluetooth. Accordingly the microcontroller guides the robot to the desired direction. The overall system showed high performance in controlling the robot movement directions.

A self-protective, reproducible textile sensor with high performance towards human–machine interactions

2019 ◽  
Vol 7 (46) ◽  
pp. 26631-26640 ◽  
Author(s):  
Ling Zhang ◽  
Jiang He ◽  
Yusheng Liao ◽  
Xuetao Zeng ◽  
Nianxiang Qiu ◽  
...  
Keyword(s):  
High Performance ◽  
Human Machine Interaction ◽  
Mechanical Durability ◽  
Textile Sensor ◽  
Electronic Textile ◽  
Machine Interaction

A self-protective, reproducible electronic textile with desirable superlyophobicity, mechanical durability and high-sensitive performance for human-machine interaction.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020

2021 ◽  
Vol 18 (14) ◽  
pp. 7567
Author(s):  
Xiaochen Zhang ◽  
Lanxin Hui ◽  
Linchao Wei ◽  
Fuchuan Song ◽  
Fei Hu
Keyword(s):  
The Elderly ◽  
Research Field ◽  
Future Research ◽  
Human Machine Interaction ◽  
Research Directions ◽  
The Past ◽  
Power Wheelchairs ◽  
The Disabled ◽  
Future Research Directions ◽  
Machine Interaction

Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.

Holistic, Context-sensitive Human-machine Interaction for Automated Vehicles

ATZ worldwide ◽  
2021 ◽  
Vol 123 (3) ◽  
pp. 46-49
Author(s):  
Tobias Hesse ◽  
Michael Oehl ◽  
Uwe Drewitz ◽  
Meike Jipp
Keyword(s):  
Automated Vehicles ◽  
Human Machine Interaction ◽  
Context Sensitive ◽  
Machine Interaction

scholarly journals Radiology Community Attitude in Saudi Arabia about the Applications of Artificial Intelligence in Radiology

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 834
Author(s):  
Magbool Alelyani ◽  
Sultan Alamri ◽  
Mohammed S. Alqahtani ◽  
Alamin Musa ◽  
Hajar Almater ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Saudi Arabia ◽  
Vital Role ◽  
Human Machine Interaction ◽  
The Future ◽  
Level Of Knowledge ◽  
Electronic Questionnaires ◽  
Machine Interaction ◽  
Applications Of Ai

Artificial intelligence (AI) is a broad, umbrella term that encompasses the theory and development of computer systems able to perform tasks normally requiring human intelligence. The aim of this study is to assess the radiology community’s attitude in Saudi Arabia toward the applications of AI. Methods: Data for this study were collected using electronic questionnaires in 2019 and 2020. The study included a total of 714 participants. Data analysis was performed using SPSS Statistics (version 25). Results: The majority of the participants (61.2%) had read or heard about the role of AI in radiology. We also found that radiologists had statistically different responses and tended to read more about AI compared to all other specialists. In addition, 82% of the participants thought that AI must be included in the curriculum of medical and allied health colleges, and 86% of the participants agreed that AI would be essential in the future. Even though human–machine interaction was considered to be one of the most important skills in the future, 89% of the participants thought that it would never replace radiologists. Conclusion: Because AI plays a vital role in radiology, it is important to ensure that radiologists and radiographers have at least a minimum understanding of the technology. Our finding shows an acceptable level of knowledge regarding AI technology and that AI applications should be included in the curriculum of the medical and health sciences colleges.

Sign in / Sign up

Export Citation Format

Share Document