Fall detection for elderly-people monitoring using learned features and recurrent neural networks
Elderly care is becoming a relevant issue with the increase of population ageing. Fall injuries, with their impact on social and healthcare cost, represent one of the biggest concerns over the years. Researchers are focusing their attention on several fall-detection algorithms. In this paper, we pre...
Main Authors: | Daniele Berardini, Sara Moccia, Lucia Migliorelli, Iacopo Pacifici, Paolo di Massimo, Marina Paolanti, Emanuele Frontoni, Adín Ramírez Rivera |
---|---|
Format: | Article |
Language: | English |
Published: |
Cambridge University Press
2020-01-01
|
Series: | Experimental Results |
Subjects: | |
Online Access: | https://www.cambridge.org/core/product/identifier/S2516712X20000039/type/journal_article |
Similar Items
-
Advances in Skeleton-Based Fall Detection in RGB Videos: From Handcrafted to Deep Learning Approaches
by: Van-Ha Hoang, et al.
Published: (2023-01-01) -
Deep-Transfer-Learning Strategies for Crop Yield Prediction Using Climate Records and Satellite Image Time-Series Data
by: Abhasha Joshi, et al.
Published: (2024-12-01) -
Self-Attention-Based BiLSTM Model for Short Text Fine-Grained Sentiment Classification
by: Jun Xie, et al.
Published: (2019-01-01) -
Bidirectional association between falls and multimorbidity in middle-aged and elderly Chinese adults: a national longitudinal study
by: Ye Tian, et al.
Published: (2024-04-01) -
Modelling Particulate Matter Using Multivariate and Multistep Recurrent Neural Networks
by: Tushar Saini, et al.
Published: (2021-12-01)