ArticleSensors (Basel, Switzerland)2018
Using Temporal Covariance of Motion and Geometric Features via Boosting for Human Fall Detection.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Exploiting the features of deep residual network with SVM classifier for human posture recognition.PloS one · 2024Article
- The Methods of Fall Detection: A Literature Review.Sensors (Basel, Switzerland) · 2023Review
- Experimental Study: Deep Learning-Based Fall Monitoring among Older Adults with Skin-Wearable Electronics.Sensors (Basel, Switzerland) · 2023Article
- Pathway of Trends and Technologies in Fall Detection: A Systematic Review.Healthcare (Basel, Switzerland) · 2022Review
- Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices.Journal of imaging · 2021Article
- Comprehensive Review of Vision-Based Fall Detection Systems.Sensors (Basel, Switzerland) · 2021Review
- Hardware/Software Co-design of Fractal Features based Fall Detection System.Sensors (Basel, Switzerland) · 2020Article
- Robust Self-Adaptation Fall-Detection System Based on Camera Height.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fall induced damages are serious incidences for aged as well as young persons. A real-time automatic and accurate fall detection system can play a vital role in timely medication care which will ultimately help to decrease the damages and complications. In this paper, we propose a fast and more accurate real-time system which can detect people falling in videos captured by surveillance cameras. Novel temporal and spatial variance-based features are proposed which comprise the discriminatory motion, geometric orientation and location of the person. These features are used along with ensemble learning strategy of boosting with J48 and Adaboost classifiers. Experiments have been conducted on publicly available standard datasets including
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.