Fatal injuries and hospitalizations caused by accidental falls are significant problems among the elderly. Detecting falls in real-time is challenging, as many falls occur in a short period. Developing an automated monitoring system that can predict falls before they happen, provide safeguards during the fall, and issue remote notifications after the fall is essential to improving the level of care for the elderly. This study proposed a concept for a wearable monitoring framework that aims to anticipate falls during their beginning and descent, activating a safety mechanism to minimize fall-related injuries and issuing a remote notification after the body impacts the ground. However, the demonstration of this concept in the study involved the offline analysis of an ensemble deep neural network architecture based on a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN) and existing data. It is important to note that this study did not involve the implementation of hardware or other elements beyond the developed algorithm. The proposed approach utilized CNN for robust feature extraction from accelerometer and gyroscope data and RNN to model the temporal dynamics of the falling process. A distinct class-based ensemble architecture was developed, where each ensemble model identified a specific class. The proposed approach was evaluated on the annotated SisFall dataset and achieved a mean accuracy of 95%, 96%, and 98% for Non-Fall, Pre-Fall, and Fall detection events, respectively, outperforming state-of-the-art fall detection methods. The overall evaluation demonstrated the effectiveness of the developed deep learning architecture. This wearable monitoring system will prevent injuries and improve the quality of life of elderly individuals.
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http://dx.doi.org/10.3390/s23104774 | DOI Listing |
Digit Health
December 2024
School of Computer Science, University of Birmingham, Birmingham, UK.
Objective: The study aims to present an active learning approach that automatically extracts clinical concepts from unstructured data and classifies them into explicit categories such as Problem, Treatment, and Test while preserving high precision and recall and demonstrating the approach through experiments using i2b2 public datasets.
Methods: Initially labeled data are acquired from a lexical-based approach in sufficient amounts to perform an active learning process. A contextual word embedding similarity approach is adopted using BERT base variant models such as ClinicalBERT, DistilBERT, and SCIBERT to automatically classify the unlabeled clinical concept into explicit categories.
Objectives: The accurate identification of Emergency Department (ED) encounters involving opioid misuse is critical for health services, research, and surveillance. We sought to develop natural language processing (NLP)-based models for the detection of ED encounters involving opioid misuse.
Methods: A sample of ED encounters enriched for opioid misuse was manually annotated and clinical notes extracted.
Phys Rev Res
April 2024
Department of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, Washington 98195, USA.
Group-equivariant neural networks have emerged as an efficient approach to model complex data, using generalized convolutions that respect the relevant symmetries of a system. These techniques have made advances in both the supervised learning tasks for classification and regression, and the unsupervised tasks to generate new data. However, little work has been done in leveraging the symmetry-aware expressive representations that could be extracted from these approaches.
View Article and Find Full Text PDFAboveground biomass (AGB) is a key indicator of crop nutrition and growth status. Accurately and timely obtaining biomass information is essential for crop yield prediction in precision management systems. Remote sensing methods play a key role in monitoring crop biomass.
View Article and Find Full Text PDFExpert Opin Drug Saf
December 2024
Department of Pharmacy, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Background: Ustekinumab is a fully human interleukin-12/23 (p40) inhibitor used to treat immune-mediated diseases. However, the limitations of clinical trials and the expanding target population necessitate an update on the ustekinumab-associated adverse events (AEs). We conducted signal mining for ustekinumab-related AEs using the United States Food and Drug Administration Adverse Event Reporting System (FAERS).
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