ArticleSensors (Basel, Switzerland)2024
Objective Falls Risk Assessment Using Markerless Motion Capture and Representational Machine Learning.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Optical to AI-Driven Markerless Motion Capture in Motor Learning and Rehabilitation.Bioengineering (Basel, Switzerland) · 2026Review
- Development, Reliability, and Validity Assessment of a Portable 3D Camera-Based System for Quantifying Postural Sway and Balance.Sensors (Basel, Switzerland) · 2026Article
- One-Legged Stand Test: Synchronized Motion Capture, Force Plate, and Radar Dataset for Fall-Risk.Scientific data · 2026Article
- Validation of a markerless motion capture app for automated scoring of sit-to-stand, timed up and go, and short physical performance battery tests in adults with chronic disease.PLOS digital health · 2026Article
- Development of an Artificial Intelligence-Based System for Predicting Fall Risk in Neurological Patients.Sovremennye tekhnologii v meditsine · 2026Article
- An Innovative IoT and Edge Intelligence Framework for Monitoring Elderly People Using Anomaly Detection on Data from Non-Wearable Sensors.Sensors (Basel, Switzerland) · 2025Article
- Falling Detection of Toddlers Based on Improved YOLOv8 Models.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls are a major issue for those over the age of 65 years worldwide. Objective assessment of fall risk is rare in clinical practice. The most common methods of assessment are time-consuming observational tests (clinical tests). Computer-aided diagnosis could be a great help. A popular clinical test for fall risk is the five times sit-to-stand. The time taken to complete the test is the most commonly used metric to identify the most at-risk patients. However, tracking the movement of skeletal joints can provide much richer insights. We use markerless motion capture, allied with a representational model, to identify those at risk of falls. Our method uses an LSTM autoencoder to derive a distance measure. Using this measure, we introduce a new scoring system, allowing individuals with differing falls risks to be placed on a continuous scale. Evaluating our method on the KINECAL dataset, we achieved an accuracy of 0.84 in identifying those at elevated falls risk. In addition to identifying potential fallers, our method could find applications in rehabilitation. This aligns with the goals of the KINECAL Dataset. KINECAL contains the recordings of 90 individuals undertaking 11 movements used in clinical assessments. KINECAL is labelled to disambiguate age-related decline and falls risk.
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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.