ArticleMedical & biological engineering & computing2017
Combining novelty detectors to improve accelerometer-based fall detection.
Article in Medical & biological engineering & computing, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fall Detection in Elderly People: A Systematic Review of Ambient Assisted Living and Smart Home-Related Technology Performance.Sensors (Basel, Switzerland) · 2025Pooled it
- Ambient assisted living for frail people through human activity recognition: state-of-the-art, challenges and future directions.Frontiers in neuroscience · 2023Review
- Falling and Drowning Detection Framework Using Smartphone Sensors.Computational intelligence and neuroscience · 2022Article
- Article
- Elderly Fall Detection Systems: A Literature Survey.Frontiers in robotics and AI · 2020Review
- Detecting Steps Walking at very Low Speeds Combining Outlier Detection, Transition Matrices and Autoencoders from Acceleration Patterns.Sensors (Basel, Switzerland) · 2017Article
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
Abstract
Research on body-worn sensors has shown how they can be used for the detection of falls in the elderly, which is a relevant health problem. However, most systems are trained with simulated falls, which differ from those of the target population. In this paper, we tackle the problem of fall detection using a combination of novelty detectors. A novelty detector can be trained only with activities of daily life (ADL), which are true movements recorded in real life. In addition, they allow adapting the system to new users, by recording new movements and retraining the system. The combination of several detectors and features enhances performance. The proposed approach has been compared with a traditional supervised algorithm, a support vector machine, which is trained with both falls and ADL. The combination of novelty detectors shows better performance in a typical cross-validation test and in an experiment that mimics the effect of personalizing the classifiers. The results indicate that it is possible to build a reliable fall detector based only on ADL.
Indexed as
Identifiers
28251444What 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.