Tailoring Activity Recognition to Provide Cues that Trigger Autobiographical Memory of Elderly People

2014 ◽  
pp. 737-742
Author(s):  
Lorena Arcega ◽  
Jaime Font ◽  
Carlos Cetina
Keyword(s):  
Autobiographical Memory ◽  
Elderly People ◽  
Activity Recognition

scholarly journals Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

2017 ◽  
Vol 34 ◽  
pp. 3-13 ◽  
Author(s):  
Miguel Ángel Álvarez de la Concepción ◽  
Luis Miguel Soria Morillo ◽  
Juan Antonio Álvarez García ◽  
Luis González-Abril
Keyword(s):  
Elderly People ◽  
Activity Recognition ◽  
Detection System ◽  
Fall Detection

scholarly journals Genetic Algorithm-Based Classifiers Fusion for Multisensor Activity Recognition of Elderly People

2015 ◽  
Vol 19 (1) ◽  
pp. 282-289 ◽  
Author(s):  
Saisakul Chernbumroong ◽  
Shuang Cang ◽  
Hongnian Yu
Keyword(s):  
Genetic Algorithm ◽  
Elderly People ◽  
Activity Recognition

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.

scholarly journals Novel Ensemble Algorithm for Multiple Activity Recognition in Elderly People Exploiting Ubiquitous Sensing Devices

2021 ◽  
pp. 1-1
Author(s):  
Sidrah Liaqat ◽  
Kia Dashtipour ◽  
Syed Aziz Shah ◽  
Ali Rizwan ◽  
Abdullah Alhumaidi Alotaibi ◽  
...  
Keyword(s):  
Elderly People ◽  
Activity Recognition ◽  
Ubiquitous Sensing ◽  
Ensemble Algorithm

scholarly journals HUMAN ACTIVITY RECOGNITION AND FALL DETECTION

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Disha Deepak Jatkar ◽  
Prof. Anil R Surve
Keyword(s):  
Elderly People ◽  
Old Age ◽  
Activity Recognition ◽  
Elderly Person ◽  
Fall Detection ◽  
Normal Activity ◽  
Main Theme ◽  
Prone Area ◽  
The Common ◽  
Common Problems

Personalized monitoring and its application is increasing with the advancement of technology. And during pandemics its become very essential to keep an eye on the prone area and one of the area to identify was old age home. Dizziness, unconsciousness, and others are the common problems associated with elderly people due to weakness and this was also the symptoms of covid. So an unusual activity of falling of elderly people was very difficult to identify and also to monitor. The technology was updated till now to identify posture of normal activity such as running, walking, jumping and many but revert to that falling was an area need to explore. During the fall of an elderly person, the injuries are very fatal, and to void this case we proposed a design to identify the fall and try to notify the system about its fall. Although we try to predict the fall so that it becomes easy to monitor and provide medical help as soon as possible. The main theme is to identify the posture activity and once identify we will compare the activity with trained datasets and if it’s normal in vision them no notification occurred and if the percentage of falls was high then we can predict the system as fall video

Sign in / Sign up

Export Citation Format

Share Document