scholarly journals RESEARCH OF HUMAN HAND MOVEMENTS REPEATABILITY USING ROBOTIC SYSTEM

2021 ◽  
Vol 3 ◽  
pp. 319-323
Author(s):  
Paulius Sakalys ◽  
Loreta Savulioniene ◽  
Dainius Savulionis
Keyword(s):  
Experimental Research ◽  
Robotic System ◽  
Hand Movements ◽  
Human Hand ◽  
Motion Recognition ◽  
Research Results ◽  
Depth Cameras ◽  
Recognition Algorithms

The aim of the research is to determine and evaluate the repeatability of the robotic system by repeating the movements of the human hand, to identify the displacement using digital infrared projection equipment, skeletal methods and depth cameras. The article reviews and selects possible skeletal methods, motion recognition algorithms, reviews and substantiates the physical equipment selected for the technical stage of the experiment. The plan of experimental research stages, research stand, systematized research results, conclusions and usability suggestions are described.

Tracking and replication of hand movements by teleguided intelligent manipulator robot

Robotica ◽  
2014 ◽  
Vol 33 (1) ◽  
pp. 141-156 ◽  
Author(s):  
A. T. Hussain ◽  
S. Faiz Ahmed ◽  
D. Hazry
Keyword(s):  
Processing Time ◽  
Robotic System ◽  
Hand Movements ◽  
Human Hand ◽  
Hand Position ◽  
3D Space ◽  
Spherical Wrist ◽  
Mathematical Formulas ◽  
Position And Orientation ◽  
Manipulator Robot

SUMMARYIn this paper, a new method is presented that allows an intelligent manipulator robotic system to track a human hand from far distance in 3D space and estimate its orientation and position in real time with the goal of ultimately using the algorithm with a robotic spherical wrist system. In this proposed algorithm, several image processing and morphology techniques are used in conjunction with various mathematical formulas to calculate the hand position and orientation. The proposed technique was tested on Remote teleguided virtual Robotic system. Experimental results show that proposed method is a robust technique in terms of the required processing time of estimation of orientation and position of hand.

EXPERIMENTAL STUDY OF THE KINEMATIC PARAMETERS OF MOVEMENT OF CARS FORCED IDLE MOTION

2018 ◽  
pp. 52-57
Author(s):  
Ivelin Kostov
Keyword(s):  
Experimental Data ◽  
Experimental Study ◽  
Experimental Research ◽  
Kinematic Parameters ◽  
Research Results ◽  
Software Product

In the work brought some experimental data of kinematic parameters of movement of cars forced idle, as the software product was used to diagnose 900 ATS, which recorded kinematic parameters of vehicle. On the basis of the conducted experimental research results are shown tabulated and analysed.

scholarly journals Human Hand Anatomy-Based Prosthetic Hand

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 137
Author(s):  
Larisa Dunai ◽  
Martin Novak ◽  
Carmen García Espert
Keyword(s):  
3D Printing ◽  
Degrees Of Freedom ◽  
Use Of Force ◽  
Hand Movements ◽  
Human Hand ◽  
Prosthetic Hand ◽  
Object Surface ◽  
Hand Phalanges ◽  
High Level

The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

Research Progress on Reinforced Concrete Laminated Slab in China

2012 ◽  
Vol 174-177 ◽  
pp. 263-267
Author(s):  
Ming Li ◽  
Zhe Zhe Sun ◽  
Wei Jian Zhao ◽  
Yong Liu
Keyword(s):  
Reinforced Concrete ◽  
Experimental Research ◽  
Concrete Structure ◽  
Early Stage ◽  
Research Progress ◽  
Horizontal Load ◽  
Reinforced Concrete Structure ◽  
Research Results ◽  
Load Carrying ◽  
New Generation

The development of new generation prefabricated reinforced concrete structure is still at an early stage in China. Reinforced concrete laminated slab, as an important horizontal load carrying member, is paid much attention to in research. Based on the research results about it in China, the progress of which is summarized, including the form, characteristics and experimental research of sandwich laminated slab, anti-ribbed laminated slab, and hollow laminated slab etc. Finally, the further research is prospected.

Research on the Characteristic of Diesel Elastic-Plate Impingement Spray

2012 ◽  
Vol 433-440 ◽  
pp. 116-122
Author(s):  
Chang Yuan Wang ◽  
Fu Shui Liu ◽  
Xiang Rong Li
Keyword(s):  
Experimental Research ◽  
Elastic Plate ◽  
High Speed ◽  
Incident Angle ◽  
Injection Pressure ◽  
High Speed Photography ◽  
Research Results ◽  
Free Jet ◽  
Constant Volume Chamber ◽  
Spray Droplets

A series of experimental research results on the characteristic of diesel elastic-plate impingement spray using High Speed Photography camera are presented in this paper. The experiments were carried out in a constant volume chamber specially designed, which can hold a high ambiance pressure. The special fixed device was designed so that the elastic-plate can be fixed on the spray path, meanwhile the spray incident angle and height can be changed. The free jet spray and elastic-plate impingement spray was compared under the same experimental condition including different injection pressure and different background pressure. Experimental research showed that impingement spray droplets diffuse more quickly than free jet spray.

Recommending suitable learning paths according to learners’ preferences: Experimental research results

2015 ◽  
Vol 51 ◽  
pp. 945-951 ◽  
Author(s):  
Eugenijus Kurilovas ◽  
Inga Zilinskiene ◽  
Valentina Dagiene
Keyword(s):  
Experimental Research ◽  
Research Results ◽  
Learning Paths

An Integrated ARMA-Based Deep Autoencoder and GRU Classifier System for Enhanced Recognition of Daily Hand Activities

2020 ◽  
pp. 2152006
Author(s):  
Patricio Rivera ◽  
Edwin Valarezo ◽  
Tae-Seong Kim
Keyword(s):  
Daily Living ◽  
Recurrent Network ◽  
Hand Movements ◽  
Human Hand ◽  
Open Door ◽  
Wearable Sensor ◽  
Human Computer Interactions ◽  
Healthcare Applications ◽  
Classifier System ◽  
Gated Recurrent Unit

Recognition of hand activities of daily living (hand-ADL) is useful in the areas of human–computer interactions, lifelogging, and healthcare applications. However, developing a reliable human activity recognition (HAR) system for hand-ADL with only a single wearable sensor is still a challenge due to hand movements that are typically transient and sporadic. Approaches based on deep learning methodologies to reduce noise and extract relevant features directly from raw data are becoming more promising for implementing such HAR systems. In this work, we present an ARMA-based deep autoencoder and a deep recurrent network (RNN) using Gated Recurrent Unit (GRU) for recognition of hand-ADL using signals from a single IMU wearable sensor. The integrated ARMA-based autoencoder denoises raw time-series signals of hand activities, such that better representation of human hand activities can be made. Then, our deep RNN-GRU recognizes seven hand-ADL based upon the output of the autoencoder: namely, Open Door, Close Door, Open Refrigerator, Close Refrigerator, Open Drawer, Close Drawer, and Drink from Cup. The proposed methodology using RNN-GRU with autoencoder achieves a mean accuracy of 84.94% and F1-score of 83.05% outperforming conventional classifiers such as RNN-LSTM, BRNN-LSTM, CNN, and Hybrid-RNNs by 4–10% higher in both accuracy and F1-score. The experimental results also showed the use of the autoencoder improves both the accuracy and F1-score of each conventional classifier by 12.8% in RNN-LSTM, 4.37% in BRNN-LSTM, 15.45% CNN, 14.6% Hybrid RNN, and 12.4% for the proposed RNN-GRU.

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