ArticleSensors (Basel, Switzerland)2018
Methods for the Real-World Evaluation of Fall Detection Technology: A Scoping Review.
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 24 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mobile health (mHealth) technologies for fall prevention among older adults in low-middle income countries: bibliometrics, network analysis and integrative review.Frontiers in digital health · 2025Pooled it
- Wearable airbag technology and machine learned models to mitigate falls after stroke.Journal of neuroengineering and rehabilitation · 2022Trial
- Simulation-to-Real Trip-Fall Detection with Continuous-Wave Doppler Radar via Physics-Informed Kinematic Modeling and Domain Randomization.Sensors (Basel, Switzerland) · 2026Article
- Fall-from-Bed Risk Prediction Using Physics-Based Bed Simulation.Sensors (Basel, Switzerland) · 2026Article
- Fall detection and pre-impact prediction technologies in older adults: a scoping review of translational maturity and public health integration.Frontiers in public health · 2026Article
- System-level interventions for fall risk assessment, fall prevention and fall injury prevention in long-term care facilities: a scoping review.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2025Article
- Biomechanical perspectives on image-based hip fracture risk assessment: advances and challenges.Frontiers in endocrinology · 2025Review
- Article
- Deep-HPI-pred: An R-Shiny applet for network-based classification and prediction of Host-Pathogen protein-protein interactions.Computational and structural biotechnology journal · 2024Article
- Near-Fall Detection in Unexpected Slips during Over-Ground Locomotion with Body-Worn Sensors among Older Adults.Sensors (Basel, Switzerland) · 2022Article
- A survey for characterizing details of fall events experienced by lower limb prosthesis users.PloS one · 2022Article
- Design of a seniors and Alzheimer's disease caring service platform.BMC medical informatics and decision making · 2021Article
- Article
- A smartphone-based online system for fall detection with alert notifications and contextual information of real-life falls.Journal of neuroengineering and rehabilitation · 2021Article
- Automated Loss-of-Balance Event Identification in Older Adults at Risk of Falls during Real-World Walking Using Wearable Inertial Measurement Units.Sensors (Basel, Switzerland) · 2021Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
- Experimental interpretation of adequate weight-metric combination for dynamic user-based collaborative filtering.PeerJ. Computer science · 2021Article
- Body-Worn Sensors for Remote Monitoring of Parkinson's Disease Motor Symptoms: Vision, State of the Art, and Challenges Ahead.Journal of Parkinson's disease · 2021Review
- Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.Sensors (Basel, Switzerland) · 2020Article
- How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls in older adults present a major growing healthcare challenge and reliable detection of falls is crucial to minimise their consequences. The majority of development and testing has used laboratory simulations. As simulations do not cover the wide range of real-world scenarios performance is poor when retested using real-world data. There has been a move from the use of simulated falls towards the use of real-world data. This review aims to assess the current methods for real-world evaluation of fall detection systems, identify their limitations and propose improved robust methods of evaluation. Twenty-two articles met the inclusion criteria and were assessed with regard to the composition of the datasets, data processing methods and the measures of performance. Real-world tests of fall detection technology are inherently challenging and it is clear the field is in its infancy. Most studies used small datasets and studies differed on how to quantify the ability to avoid false alarms and how to identify non-falls, a concept which is virtually impossible to define and standardise. To increase robustness and make results comparable, larger standardised datasets are needed containing data from a range of participant groups. Measures that depend on the definition and identification of non-falls should be avoided. Sensitivity, precision and F-measure emerged as the most suitable robust measures for evaluating the real-world performance of fall detection systems.
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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.