scholarly journals Estimation of Emptying Urinary Bladder in Paraplegic and Elderly People Based on Bioimpedance, Hypogastric Region Temperature and Neural Network

2018 ◽  
pp. 931-935 ◽  
Author(s):  
Michael Rodas ◽  
Layla Amoroso ◽  
Mónica Huerta
Keyword(s):  
Neural Network ◽  
Urinary Bladder ◽  
Elderly People ◽  
Region Temperature

scholarly journals In-House Deep Environmental Sentience for Smart Homecare Solutions toward Ageing Society

2020 ◽  
Author(s):  
PHILIP EASOM ◽  
Ahmed Bouridane ◽  
Feiyu Qiang; ◽  
Li Zhang ◽  
Carolyn Downs ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Home Care ◽  
Convolutional Neural Network ◽  
Elderly People ◽  
Maximum Speed ◽  
Base System ◽  
Ageing Society ◽  
Care Workers ◽  
Real Time Applications

<p>With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated.<i></i></p>

Video-Based Human Activity Recognition for Elderly Using Convolutional Neural Network

2020 ◽  
Vol 12 (1) ◽  
pp. 36-48
Author(s):  
Vijayaprabakaran K. ◽  
Sathiyamurthy K. ◽  
Ponniamma M.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Elderly People ◽  
Activity Recognition ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Vision

A typical healthcare application for elderly people involves monitoring daily activities and providing them with assistance. Automatic analysis and classification of an image by the system is difficult compared to human vision. Several challenging problems for activity recognition from the surveillance video involving the complexity of the scene analysis under observations from irregular lighting and low-quality frames. In this article, the authors system use machine learning algorithms to improve the accuracy of activity recognition. Their system presents a convolutional neural network (CNN), a machine learning algorithm being used for image classification. This system aims to recognize and assist human activities for elderly people using input surveillance videos. The RGB image in the dataset used for training purposes which requires more computational power for classification of the image. By using the CNN network for image classification, the authors obtain a 79.94% accuracy in the experimental part which shows their model obtains good accuracy for image classification when compared with other pre-trained models.

Neural network analysis in predicting 2-year survival in elderly people: a new statistical–mathematical approach

2001 ◽  
Vol 32 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Mauro Cacciafesta ◽  
Fabio Campana ◽  
Gianfranco Piccirillo ◽  
Paolo Cicconetti ◽  
Ilaria Trani ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Analysis ◽  
Elderly People ◽  
Mathematical Approach ◽  
Neural Network Analysis

scholarly journals In-House Deep Environmental Sentience for Smart Homecare Solutions toward Ageing Society

2020 ◽  
Author(s):  
PHILIP EASOM ◽  
Ahmed Bouridane ◽  
Feiyu Qiang; ◽  
Li Zhang ◽  
Carolyn Downs ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Home Care ◽  
Convolutional Neural Network ◽  
Elderly People ◽  
Maximum Speed ◽  
Base System ◽  
Ageing Society ◽  
Care Workers ◽  
Real Time Applications

<p>With an increasing amount of elderly people needing home care around the clock, care workers are not able to keep up with the demand of providing maximum support to those who require it. As medical costs of home care increase the quality is care suffering as a result of staff shortages, a solution is desperately needed to make the valuable care time of these workers more efficient. This paper proposes a system that is able to make use of the deep learning resources currently available to produce a base system that could provide a solution to many of the problems that care homes and staff face today. Transfer learning was conducted on a deep convolutional neural network to recognize common household objects was proposed. This system showed promising results with an accuracy, sensitivity and specificity of 90.6%, 0.90977 and 0.99668 respectively. Real-time applications were also considered, with the system achieving a maximum speed of 19.6 FPS on an MSI GTX 1060 GPU with 4GB of VRAM allocated.<i></i></p>

scholarly journals Human Fall Detection Using Video Surveillance

2021 ◽  
Vol 1 (1) ◽  
pp. 1-10
Author(s):  
Kavya G ◽  
Sunil Kumar C T ◽  
Dhanush C ◽  
Kruthika J
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Elderly People ◽  
Background Subtraction ◽  
Learning Algorithm ◽  
Fall Detection ◽  
Severe Injuries ◽  
Deep Learning Algorithm ◽  
Human Movements ◽  
Proposed Model

Fall is one of the biggest challenge in elderly people, pregnant and small children’s, who stays alone in home. Sometimes this fall leads to severe injuries and even to death. Detecting the fall is very much important for elderly people. Convolutional Neural Network (CNN) is an deep learning algorithm used for image processing. In this paper, we present a video-based fall detection using CNN, this CNN will perform background subtraction and captures only foreground objects to detect the human movements and detect if fall happens. Firstly, camera will be capturing all the movements of the person. Our proposed model will detect the fall and finally an alarm is raised and email is sent to a given particular caretaker and family member. Our experimental results show the best performance of the proposed model.

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