scholarly journals Development of a Chinese Chess Robotic System for the Elderly Using Convolutional Neural Networks

2020 ◽  
Vol 12 (10) ◽  
pp. 3980 ◽  
Author(s):  
Pei-Jarn Chen ◽  
Szu-Yueh Yang ◽  
Chung-Sheng Wang ◽  
Muslikhin Muslikhin ◽  
Ming-Shyan Wang
Keyword(s):  
Neural Networks ◽  
Elderly People ◽  
Convolutional Neural Networks ◽  
Great Influence ◽  
The Elderly ◽  
Robotic Arm ◽  
Life Long Learning ◽  
Dementia Patients ◽  
Reduce Risk ◽  
Aging Societies

According to the data from Alzheimer’s Disease International (ADI) in 2018, it is estimated that 10 million new dementia patients will be added worldwide, and the global dementia population is estimated to be 50 million. Due to a decline in the birth rate and the development and great progress of medical technology, the proportion of elderly people has risen annually in Taiwan. In fact, Taiwan has become one of the fastest-growing aged countries in the world. Consequently, problems related to aging societies will emerge. Dementia is one of most prevailing aging-related diseases, with a great influence on daily life and a great economic burden. Dementia is not a single disease, but a combination of symptoms. There is currently no medicine that can cure dementia. Finding preventive measures for dementia has become a public concern. Older people should actively increase brain-protective factors and reduce risk factors in their lives to reduce the risk of dementia and even prevent the occurrence of dementia. Studies have shown that engaging in mental or creative activities that stimulate brain function has a relative risk reduction of nearly 50%. Elderly people should develop the habit of life-long learning to strengthen effective neural bonds between brain cells and preserve brain cognitive functions. Playing chess is one of the suggested activities. This paper aimed to develop a Chinese robotic chess system for the elderly. It mainly uses a camera to capture the contour of the Chinese chessman, recognizes the character and location of the chessman, and then transmits this information to the robotic arm, which will grab and place the chessman in the appropriate position on the chessboard. The camera image is transmitted to MATLAB for image recognition. The character of the chessman is recognized by convolutional neural networks (CNNs). Forward and inverse kinematics are used to manipulate the robotic arm. Even if the chessmen are arbitrarily placed, the experiment showed that their coordinates can be found through the camera as long as they are located within the working scope of the camera and the robotic arm. For black chessmen, no matter how many degrees they are rotated, they can be recognized correctly, while the red ones can be recognized 100% of the time within 90° of rotation and 98.7% with more than a 90° rotation.

scholarly journals An Economical Robotic Arm for Playing Chess Using Visual Servoing

2020 ◽  
Vol 2 (3) ◽  
pp. 141-146
Author(s):  
Dr. Ranganathan G.
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Data Processing ◽  
Convolutional Neural Networks ◽  
Visual Servoing ◽  
Mechanical Design ◽  
Robotic Arm ◽  
Game Engine ◽  
Chess Game

The proposed paper outlines the design of an economical robotic arm which is used to visualize the chess board and play with the opponent using visual servoing system. We have used the FaBLab RUC's mechanical design prototype proposed and have further used Solidworks software to design the 4 jointed gripper. The proposed methodology involves detecting the squares on the corners of the chessboard and further segmenting the images. This is followed by using convolutional neural networks to train and recognize the image in order to determine the movement of the chess pieces. To trace the manipulator, Kanade-Lucas-Tomasi method is used in the visual servoing system. An Arduino uses Gcode commands to interact with the robotic arm. Game Decisions are taken with the help of chess game engine the pieces on the board are moved accordingly. Thus a didactic robotic arm is developed for decision making and data processing, serving to be a good opponent in playing chess.

HUBUNGAN DUKUNGAN KELUARGA, PENGETAHUAN, DENGAN KEAKTIFAN LANJUT USIA (Lansia) MENGIKUTI KEGIATAN POSYANDU LANSIA DI WILAYAH PUSKESMAS GADING SURABAYA

2020 ◽  
Vol 4 (1) ◽  
pp. 1-6
Author(s):  
NINDY VARA MEIGIA
Keyword(s):  
Health Center ◽  
Family Support ◽  
Elderly People ◽  
Simple Random Sampling ◽  
The Elderly ◽  
Design Data ◽  
Cross Sectional ◽  
Bivariate Data ◽  
Quality Regulation ◽  
Cross Sectional Design

Minimum Standards Service is basic service quality regulation that is entitled to be obtained by every citizen. Elderly people must obtain 100% health services. Various factors can lead to low visits to the elderly posyandu in the Gading Puskesmas area in 2017. Objective: to find out the relationship between family support, knowledge, and elderly activities in the elderly posyandu in Gading Surabaya puskesmas. Method: The type of research is observational analytic with a cross-sectional design. Data processing and analysis is done by univariate and bivariate. Data collection techniques by conducting simple random sampling with a sample of 94 elderly people. Location and time of research from December 2018 at the posyandu in the Gading Surabaya health center area. Results: There is a significant value of 0,000 <0,05, so it can be concluded is have a relationship with family support, knowledge of elderly with activity following the elderly posyandu at Gading Surabaya health center

Forms of physical activity of the elderly

2020 ◽  
Vol 18 (3) ◽  
pp. 221-228
Author(s):  
Piotr Czarnecki ◽  
◽  
Justyna Podgórska-Bednarz ◽  
Lidia Perenc ◽  
◽  
...  
Keyword(s):  
Physical Activity ◽  
Elderly People ◽  
Population Aging ◽  
Human Life ◽  
Good Health ◽  
The Elderly ◽  
Polish Society ◽  
Central Statistical ◽  
Key Phrases ◽  
The Impact

Introduction. Physical activity is known to be an important factor influencing health throughout human life. This issue has become crucial for public health due to the aging of the population in both developed and developing countries. Aim. is to present a literature review on the forms of physical activity undertaken by the elderly, as well as on issues related to physical activity and the population aging. Material and methods. The study was prepared on the basis of a review of Polish and foreign literature. The following databases and data sources were used: EBSCO, ScienceDirect and Google Scholar. An additional source of data were the websites of the Central Statistical Office. Strictly defined key phrases were used during the collection of literature. The work has been divided into thematic subsections on the aging of the society, the impact of physical activity on health and the main topic, i.e. forms of physical activity selected by the elderly. Analysis of the literature. The number of elderly people in Polish society has increased by almost 3.7 million over three decades. Therefore, an important topic is prophylaxis aimed at increasing the number of days in good health, largely covering the broadly understood activation of the elderly. The available data indicate that only 12% of elderly people undertake physical activity once a week. The most common form of spending free time actively is walking (as many as 73% of people in this population declare this form of physical activity in one of the presented studies). Conclusion. Organized forms of physical activity are undertaken much less frequently by the analyzed age group mainly due to financial limitations and limited availability of sports infrastructure.

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Sign in / Sign up

Export Citation Format

Share Document