scholarly journals Development of a Carbon Fiber Knitted Capacitive Touch Sensor

MRS Advances ◽  
2016 ◽  
Vol 1 (38) ◽  
pp. 2641-2651 ◽  
Author(s):  
Richard Vallett ◽  
Ryan Young ◽  
Chelsea Knittel ◽  
Youngmoo Kim ◽  
Genevieve Dion
Keyword(s):  
Human Machine Interaction ◽  
Electrically Conductive ◽  
Touch Sensor ◽  
Medical Textiles ◽  
Conductive Yarns ◽  
Embedded Electronics ◽  
Smart Garments ◽  
Knitted Structure ◽  
Fabric Surface ◽  
Machine Interaction

ABSTRACTTextiles, in combination with advances in materials and design, offer exciting new possibilities for human and environmental interaction, including biometric and touch-based sensing. Previous fabric-based or flexible touch sensors have generally required a large number of sensing electrodes positioned in a dense XY grid configuration and a multitude of wires. This paper investigates the design and manufacturing of a planar (two-dimensional, XY location) touch fabric sensor with only two electrodes (wires) to sense both planar touch and pressure, making it ideal for applications with limited space/complexity for wiring. The proposed knitted structure incorporates a supplementary method of sensing to detect human touch on the fabric surface, which offers advantages over previous methods of touch localization through an efficient use of wire connections and sensing materials. This structure is easily manufactured as a single component utilizing flatbed knitting techniques and electrically conductive yarns. The design requires no embedded electronics or solid components in the fabric, which allows the sensor to be flexible and resilient. This paper discusses the design, fabrication, sensing methods, and applications of the fabric sensor in robotics and human-machine interaction, smart garments, and wearables, as well as the highly transdisciplinary approach pursued in developing medical textiles and flexible embedded sensors.

scholarly journals The Influence of Electro-Conductive Compression Knits Wearing Conditions on Heating Characteristics

Materials ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 6780
Author(s):  
Md. Reazuddin Repon ◽  
Ginta Laureckiene ◽  
Daiva Mikucioniene
Keyword(s):  
Heat Generation ◽  
Chemical Interaction ◽  
Morphological Changes ◽  
Surface Damage ◽  
Electrically Conductive ◽  
Conductive Yarns ◽  
Generating Capacity ◽  
Rib Structure ◽  
Knitted Structure ◽  
Conductive Yarn

Textile-based heaters have opened new opportunities for next-generation smart heating devices. This experiment presents electrically conductive textiles for heat generation in orthopaedic compression supports. The main goal was to investigate the influence of frequent washing and stretching on heat generation durability of constructed compression knitted structures. The silver coated polyamide yarns were used to knit a half-Milano rib structure containing elastomeric inlay-yarn. Dimensional stability of the knitted fabric and morphological changes of the silver coated electro-conductive yarns were investigated during every wash cycle. The results revealed that temperature becomes stable within two minutes for all investigated fabrics. The heat generation was found to be dependent on the stretching, mostly due to the changing surface area; and it should be considered during the development of heated compression knits. Washing negatively influences the heat-generating capacity on the fabric due to the surface damage caused by the mechanical and chemical interaction during washing. The higher number of silver-coated filaments in the electro-conductive yarn and the knitted structure, protecting the electro-conductive yarn from mechanical abrasion, may ensure higher durability of heating characteristics.

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.

scholarly journals Human Factors Modelling Approach: Application to a Safety Device Supporting Crane Operations in Major Hazard Industries

2021 ◽  
Vol 13 (4) ◽  
pp. 2304
Author(s):  
Maria Francesca Milazzo ◽  
Giuseppa Ancione ◽  
Giancarlo Consolo
Keyword(s):  
Systematic Approach ◽  
New Technologies ◽  
Health And Safety ◽  
Safety Device ◽  
Human Machine Interaction ◽  
Safety And Health ◽  
Emotional Strain ◽  
Smart Safety ◽  
Machine Interaction ◽  
Modelling Approach

The European Directive on Safety and Health at Work and the following normatives have the scope to provide high levels of health and safety at work, based on some general principles managing activities and including the risk assessment to continuously improve processes and workplaces. However, the working area changes and brings new risks and challenges for workers. Several of them are associated with new technologies, which determine complex human–machine interactions, leading to an increased mental and emotional strain. To reduce these emerging risks, their understanding and assessment are important. Although great efforts have already been made, there is still a lack of conceptual frameworks for analytically assessing human–machine interaction. This paper proposes a systematic approach that, beyond including the classification in domains to explain the complexity of the human–machine interaction, accounts for the information processing of the human brain. Its validation is shown in a major accident hazard industry where a smart safety device supporting crane related operations is used. The investigation is based on the construction of a questionnaire for the collection of answers about the feeling of crane operators when using the device and the evaluation of the Cronbach’s alpha to measure of the reliability of the assessment.

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