A low-cost real time virtual system for postural stability assessment at home

2014 ◽  
Vol 117 (2) ◽  
pp. 322-333 ◽  
Author(s):  
Giuseppe Placidi ◽  
Danilo Avola ◽  
Marco Ferrari ◽  
Daniela Iacoviello ◽  
Andrea Petracca ◽  
...  
Keyword(s):  
Real Time ◽  
Postural Stability ◽  
Low Cost ◽  
Stability Assessment ◽  
Virtual System ◽  
At Home

scholarly journals Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons

2022 ◽  
pp. 9-17
Author(s):  
Nagumi Wambui
Keyword(s):  
Real Time ◽  
Health Monitoring ◽  
Health Management ◽  
Low Cost ◽  
Cost Effective ◽  
Sensor Technology ◽  
Elderly Persons ◽  
Physiological Indicators ◽  
The Individual ◽  
At Home

This research gives an overview of numerous kinds of identification and sensor technology that have been shown to improve the standard of living of older persons in hospital and home settings. Recent advancements in semiconductors and microsystems have enabled the creation of low-cost medical equipment, which are used by various persons as prevention and E-Health Monitoring (EHM) tools. Remote health management, which relies on wearable and non-invasive sensing devices, controllers, and current information and communication technology, provides cost-effective solutions that enable individuals to remain in their familiar homes while being safeguarded. Additionally, when preventative actions are implemented at home, costly medical centers are becoming available for use by intensive care patients. Patients' vital physiological indicators may be monitored in real time by remote devices, which can also watch, analyze, and, most importantly, offer feedback on their health problems. To translate different types of vital indicators into electrical impulses, sensors are employed in computerized healthcare and non-medical devices. Life-sustaining implants, preventative interventions, and long-term E-Health Monitoring (EHM) of handicapped or unwell patients may all benefit from sensors. Whether the individual is in a clinic, hospital, or at home, medical businesses, such as health insurers, want real-time, dependable, and precise diagnostic findings from sensing devices that can be examined virtually.

LOW COST REAL TIME ROCKET TELEMETRY SYSTEM DESIGN

2019 ◽  
Author(s):  
Gabriel de Almeida Souza ◽  
Larissa Barbosa ◽  
Glênio Ramalho ◽  
Alexandre Zuquete Guarato
Keyword(s):  
System Design ◽  
Real Time ◽  
Low Cost ◽  
Telemetry System

Low-Cost High-Speed Techniques for Real-Time Simulation of Power Electronic Systems

2007 ◽  
Author(s):  
R. E. Crosbie ◽  
J. J. Zenor ◽  
R. Bednar ◽  
D. Word ◽  
N. G. Hingorani
Keyword(s):  
Real Time ◽  
High Speed ◽  
Low Cost ◽  
Electronic Systems ◽  
Power Electronic ◽  
Real Time Simulation ◽  
Time Simulation

scholarly journals Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Yong He ◽  
Hong Zeng ◽  
Yangyang Fan ◽  
Shuaisheng Ji ◽  
Jianjian Wu
Keyword(s):  
Deep Learning ◽  
Integrated Pest Management ◽  
Pest Management ◽  
Real Time ◽  
Oilseed Rape ◽  
Data Augmentation ◽  
Low Cost ◽  
Response Speed ◽  
Original Model ◽  
Real Time System

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).

scholarly journals Low-Cost Embedded System Using Convolutional Neural Networks-Based Spatiotemporal Feature Map for Real-Time Human Action Recognition

2021 ◽  
Vol 11 (11) ◽  
pp. 4940
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Embedded System ◽  
Real Time ◽  
Action Recognition ◽  
Processing Speed ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Action Recognition ◽  
Human Action ◽  
Video Data ◽  
Feature Maps

The field of research related to video data has difficulty in extracting not only spatial but also temporal features and human action recognition (HAR) is a representative field of research that applies convolutional neural network (CNN) to video data. The performance for action recognition has improved, but owing to the complexity of the model, some still limitations to operation in real-time persist. Therefore, a lightweight CNN-based single-stream HAR model that can operate in real-time is proposed. The proposed model extracts spatial feature maps by applying CNN to the images that develop the video and uses the frame change rate of sequential images as time information. Spatial feature maps are weighted-averaged by frame change, transformed into spatiotemporal features, and input into multilayer perceptrons, which have a relatively lower complexity than other HAR models; thus, our method has high utility in a single embedded system connected to CCTV. The results of evaluating action recognition accuracy and data processing speed through challenging action recognition benchmark UCF-101 showed higher action recognition accuracy than the HAR model using long short-term memory with a small amount of video frames and confirmed the real-time operational possibility through fast data processing speed. In addition, the performance of the proposed weighted mean-based HAR model was verified by testing it in Jetson NANO to confirm the possibility of using it in low-cost GPU-based embedded systems.

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