ArticleSensors (Basel, Switzerland)2020
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 67 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.GeroScience · 2024Pooled it
- Article
- Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Wearables for Telemonitoring in ATTR-Amyloidosis: Current Perspectives.Sensors (Basel, Switzerland) · 2026Review
- Effects of Levodopa on Gait in People With Parkinson's Disease With and Without a History of Falls.Movement disorders clinical practice · 2025Article
- Prophylactic fixation in elderly fractures: Preventive breakthrough or unnecessary intervention?World journal of orthopedics · 2025Review
- Advancing the Integration of Digital Health Technologies in the Drug Development Ecosystem.Journal of medical Internet research · 2025Article
- User groups of inpatient multidisciplinary therapies for Parkinson's disease in Germany: a bicenter prospective observational study.Neurological research and practice · 2025Article
- Validity and reliability of inertial measurement units for measuring gait kinematics in older adults across varying fall risk levels and walking speeds.BMC geriatrics · 2025Article
- Regulatory Pathways for Qualification and Acceptance of Digital Health Technology-Derived Clinical Trial Endpoints: Considerations for Sponsors.Clinical pharmacology and therapeutics · 2025Review
- Predicting future fallers in Parkinson's disease using kinematic data over a period of 5 years.NPJ digital medicine · 2024Article
- Differences in Trunk Acceleration-Derived Gait Indexes in Stroke Subjects with and without Stroke-Induced Immunosuppression.Sensors (Basel, Switzerland) · 2024Article
- Economic Perspective of the Use of Wearables in Health Care: A Systematic Review.Mayo Clinic proceedings. Digital health · 2024Review
- Walking on a Balance Beam as a New Measure of Dynamic Balance to Predict Falls in Older Adults and Patients with Neurological Conditions.Sports medicine - open · 2024Article
- Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.Sensors (Basel, Switzerland) · 2024Article
- Clinical and device-based predictors of improved experience of activities of daily living after a multidisciplinary inpatient treatment for people with Parkinson's disease: a cohort study.Therapeutic advances in neurological disorders · 2024Article
- Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.Sensors (Basel, Switzerland) · 2023Article
- Association between daily gait speed patterns and cognitive impairment in community-dwelling older adults.Scientific reports · 2023Article
- Systematic review of candidate prognostic factors for falling in older adults identified from motion analysis of challenging walking tasks.European review of aging and physical activity : official journal of the European Group for Research into Elderly and Physical Activity · 2023Review
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 3 countries.
Funding
Abstract
Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.
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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.