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.

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 An Intelligent Adaptive cMOOC “IACM” for Improving Learner’s Engagement

2021 ◽  
Vol 16 (13) ◽  
pp. 82
Author(s):  
Soumaya El Emrani ◽  
Ali El Merzouqi ◽  
Mohamed Khaldi
Keyword(s):  
Learning Styles ◽  
Statistical Data ◽  
Intelligent System ◽  
Online Course ◽  
Human Machine Interaction ◽  
Massive Open Online Course ◽  
Regression Algorithms ◽  
Massive Number ◽  
Machine Interaction ◽  
Very High

Despite the massive number of enrollments in MOOC (Massive Open Online Course) platforms, dropout rates are very high. This problem can be due to several factors: Social, pedagogical, prior knowledge as well as a demotivation. To deal with this type of problems, we have designed an adaptive cMOOC (Connectivist MOOC) platform for each registered learner’s profile. From the first human-machine interaction, the process adapts the learner's need according to a pre-established model. It is based on the processing of statistical data collected by correspondence analysis and regression algorithms. Each generated learner’s profile will provide an adaptive navigation and pedagogical activities. The intelligent system presented in this work will be able to classify learners according to their preferences and learning styles.

scholarly journals The Analysis of the Possibility of Using Viola-Jones Algorithm to Recognise Hand Gestures in Human-Machine Interaction

2017 ◽  
Vol 40 (1) ◽  
pp. 109-144
Author(s):  
Piotr Golański ◽  
Marek Szczekala
Keyword(s):  
Image Recognition ◽  
Wearable Computers ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Specific Location ◽  
Computer Aided ◽  
Machine Interaction

AbstractThe article concerns the issue of applying computer-aided systems of the maintenance of technical objects in difficult conditions. Difficult conditions shall be understood as these in which the maintenance takes place in a specific location making it hard or even preventing from using a computer. In these cases computers integrated with workwear should be used, the so-called wearable computers, with which the communication is possible by using hand gestures. The results of the analysis of the usefulness of one of methods of image recognition based on Viola-Jones algorithm were described. This algorithm enables to obtain the model of recognised image which might be used as a pattern in the application programme detecting a certain image.

scholarly journals Analysis of Human-Machine Interaction Through Facial Expression and Hand-Gesture Recognition

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1320-1328
Keyword(s):  
Facial Expression ◽  
Human Brain ◽  
Recent Work ◽  
Facial Expressions ◽  
Gesture Recognition ◽  
Oral Communication ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Human Machine Interaction ◽  
Machine Interaction

This paper focuses on a review of recent work on facial expression and hand gesture recognitions. Facial expressions and hand gestures are used to express emotions without oral communication. The human brain has the ability to identify the emotions of persons using expressions or hand gestures within a fraction of a second. Research has been conducted on human–machine interactions (HMIs), and the expectation is that systems based on such HMI algorithms should respond similarly. Furthermore, when a person intends to express emotions orally, he or she automatically uses complementary facial expressions and hand gestures. Extant systems are designed to express these emotions through HMIs without oral communication. Other systems have added various combinations of hand gestures and facial expressions as videos or images. The meaning or emotions conveyed by particular hand gestures and expressions are predefined in these cases. Accordingly, the systems were trained and tested. Further, certain extant systems have separately defined the meanings of such hand gestures and facial expressions

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.

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