The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Challenges, issues and trends in fall detection systems
Open accessAbstract
Since falls are a major public health problem among older people, the number of systems aimed at detecting them has increased dramatically over recent years. This work presents an extensive literature review of fall detection systems, including comparisons among various kinds of studies. It aims to serve as a reference for both clinicians and biomedical engineers planning or conducting field investigations. Challenges, issues and trends in fall detection have been identified after the reviewing work. The number of studies using context-aware techniques is still increasing but there is a new trend towards the integration of fall detection into smartphones as well as the use of machine learning methods in the detection algorithm. We have also identified challenges regarding performance under real-life conditions, usability, and user acceptance as well as issues related to power consumption, real-time operations, sensing limitations, privacy and record of real-life falls.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Kangas, M., Vikman, I., Nyberg, L., Korpelainen, R., Lindblom, J., & Jämsä, T. (2012) · Gait & Posture · doi:10.1016/j.gaitpost.2011.11.016 · Cited by 126
Cited by
Human fall detection on embedded platform using depth maps and wireless accelerometer
Kwolek, B., & Kepski, M. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.09.005 · Cited by 650
Remote patient monitoring: a comprehensive study
Malasinghe, L. P., Ramzan, N., & Dahal, K. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-017-0598-x · Cited by 534
SisFall: A Fall and Movement Dataset
Sucerquia, A., López, J., & Vargas-Bonilla, J. (2017) · Sensors · doi:10.3390/s17010198 · Cited by 530
Elderly Fall Detection Systems: A Literature Survey
Wang, X., Ellul, J., & Azzopardi, G. (2020) · Frontiers in Robotics and AI · doi:10.3389/frobt.2020.00071 · Cited by 380
Home Camera-Based Fall Detection System for the Elderly
De Miguel, K., Brunete, A., Hernando, M., & Gambao, E. (2017) · Sensors · doi:10.3390/s17122864 · Cited by 267
Metadata
- Authors
- Igual, R., Medrano, C., & Plaza, I.
- Venue
- BioMedical Engineering OnLine
- Published
- 2013
- Volume
- 12
- Issue
- 1
- Pages
- 66
- ISSN
- 1475-925X (ISSN-L)
- OpenAlex
- W2155326828
- Cited by
- 654
- References
- 1
- FWCI
- 52.04
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Challenges, issues and trends in fall detection systems. https://app.cochat.ai/papers/10.1186/1475-925x-12-66
- The paper
- Igual, R., Medrano, C., & Plaza, I. (2013). Challenges, issues and trends in fall detection systems. BioMedical Engineering OnLine. https://doi.org/10.1186/1475-925x-12-66
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
