ArticleJournal of rehabilitation and assistive technologies engineering
Falls are unintentional: Studying simulations is a waste of faking time.
Article in Journal of rehabilitation and assistive technologies engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 22 citations in OpenAlex.
- A Multi-Head Attention Transformer Model for Wearable in Situ Fall Detection.IEEE access : practical innovations, open solutions · 2026Article
- Energy-Efficient Fall-Detection System Using LoRa and Hybrid Algorithms.Biomimetics (Basel, Switzerland) · 2025Article
- Enhancing Slip, Trip, and Fall Prevention: Real-World Near-Fall Detection with Advanced Machine Learning Technique.Sensors (Basel, Switzerland) · 2025Article
- Investigating the biomechanics of falls in older adults in long-term care using a video camera: a scoping review.BMC geriatrics · 2024Article
- Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.Sensors (Basel, Switzerland) · 2024Article
- Falls in people post-Guillain-Barré syndrome in the United Kingdom: A national cross-sectional survey of community based adults.Health & social care in the community · 2022Article
- Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.Sensors (Basel, Switzerland) · 2021Article
- Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Researchers tend to agree that falls are, by definition, unintentional and that sensor algorithms (the processes that allows a computer program to identify a fall among data from sensors) perform poorly when attempting to detect falls 'in the wild' (a phrase some scientists use to mean 'in reality'). Algorithm development has been reliant on simulation, i.e. asking actors to throw themselves intentionally to the ground. This is unusual (no one studies faked coughs or headaches) and uninformative (no one can intend the unintentional). Researchers would increase their chances of detecting 'real' falls in 'the real world' by studying the behaviour of fallers, however, vulnerable, before, during and after the event: the literature on the circumstances of falling is more informative than any number of faked approximations. A complimentary knowledge base (in falls, sensors and/or signals) enables multidisciplinary teams of clinicians, engineers and computer scientists to tackle fall detection and aim for fall prevention. Throughout this paper, I discuss differences between falls, 'intentional falling' and simulations, and the balance between simulation and reality in falls research, finally suggesting ways in which researchers can access examples of falls without resorting to fakery.
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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.