ArticleScientific reports2021
XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 3 syntheses or guidelines pooled it, 75 citations in OpenAlex.
- Interpretable and explainable artificial intelligence for wearable sensor-based fall risk assessment in older adults: a systematic review with considerations for prosthetics and orthotics.Frontiers in computational neuroscience · 2026Pooled it
- Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection.Journal of neuroengineering and rehabilitation · 2024Pooled it
- The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Increased Gait Variability while Texting and Walking with Added Cognitive Loads in Challenging Environments.Journal of musculoskeletal & neuronal interactions · 2026Article
- Evaluation and Comparison of Machine Learning Methods for Type 2 Diabetes Classification and Associated Factors.Healthcare informatics research · 2026Article
- Explainable Machine Learning Using Sensor-Derived Biomechanical Features to Classify Elevated VALR-Related Loading Across Midsole Hardness Conditions in School-Aged Boys.Sensors (Basel, Switzerland) · 2026Article
- Meta-heuristic and machine learning based functional capacity prediction using gait parameters in patients with heart failure.Scientific reports · 2026Article
- Challenges and potential of using digital biomarkers in healthcare and clinical trials.Communications medicine · 2026Review
- Predicting first falls among older adults with chronic conditions and polypharmacy using routinely collected health records.Frontiers in artificial intelligence · 2026Article
- DRPM: An advanced predictive model for early diabetes detection and risk stratification.Molecular therapy. Nucleic acids · 2025Article
- Classifying simulated gait impairments using privacy-preserving explainable artificial intelligence and mobile phone videos.PLOS digital health · 2025Article
- Improving the Accuracy of a Wearable Uroflowmeter for Incontinence Monitoring Under Dynamic Conditions: Leveraging Machine Learning Methods.Biosensors · 2025Article
- A scoping review of machine learning models to predict risk of falls in elders, without using sensor data.Diagnostic and prognostic research · 2025Review
- Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis.Journal of functional morphology and kinesiology · 2025Article
- Early detection of autism spectrum disorder: gait deviations and machine learning.Scientific reports · 2025Article
- Gait stability prediction through synthetic time-series and vision-based data.Frontiers in sports and active living · 2025Article
- Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.PloS one · 2025Article
- Determinants and risk prediction models for frailty among community-living older adults in eastern China.Frontiers in public health · 2025Article
- A Machine-Learning Model of Chronological Age Based on Routine Blood Biomarkers in a Central European Population: A Potential Biological Age Marker.Journal of aging research · 2025Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63-89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inertial measurement unit sensors embedded in the shoe-type data loggers on both outsoles. A metric was defined to classify the fall risks, determined based on a set of questions determining the history of falls and fear of falls. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. Moreover, the definition of the fall levels was classified into high- and low-risk groups. At all speeds, three gait features were identified with the XGBoost (stride length, walking speed, and stance phase) that accurately classified the fall risk levels. The model accuracy in classifying fall risk levels ranged between 67-70% with 43-53% sensitivity and 77-84% specificity. Thus, we identified the optimal gait features for accurate fall risk level classification in older adults. The XGBoost model could inspire future works on fall prevention and the fall-risk assessment potential through the gait analysis of older adults.
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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.