| Title |
Automatic Fall Detection For The Care Of Older Adults In Smart Cities |
| ID_Doc |
11321 |
| Authors |
Dueñas S.J.R.; Mejia J.; Ochoa A.; Díaz J.; Rascon L.; Gordillo N.; Sánchez-DelaCruz E. |
| Year |
2022 |
| Published |
Studies in Systems, Decision and Control, 347 |
| DOI |
http://dx.doi.org/10.1007/978-3-030-68663-5_4 |
| Abstract |
As the number of elderly people increases, it is a necessity for smart cities to take care of elder special needs. As people age, the likelihood of accidents increases because of their motor skills decrease over time, this risk is not only latent when they live alone but also exists within the nursing homes. Because of this, constant care for older adults is a necessity for smart cities, this may not always be possible due to the lack of family members who can care for the elder or in the case of the nursing homes, the staff may not be enough to care for all adults. This leaves the need for systems that can constantly monitor older adults and respond and alert automatically in the event of accidents. In order to find a means to improve the quality of life of older adults who may suffer accidents, in this chapter it is presented an algorithm based on neural networks for the automatic fall detection of older adults in nursing homes. The implemented model was trained and tested on a database of video images containing fall situations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. |
| Author Keywords |
CNNs architecture; Fall detection; Smart city |
