ArticleSensors (Basel, Switzerland)2017
Faller Classification in Older Adults Using Wearable Sensors Based on Turn and Straight-Walking Accelerometer-Based Features.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 6 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 6 syntheses or guidelines pooled it, 62 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- Development Technologies for the Monitoring of Six-Minute Walk Test: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- Wearable Inertial Sensors to Assess Gait during the 6-Minute Walk Test: A Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Usefulness of Mobile Devices in the Diagnosis and Rehabilitation of Patients with Dizziness and Balance Disorders: A State of the Art Review.Clinical interventions in aging · 2020Pooled it
- Wearable Sensor Data to Track Subject-Specific Movement Patterns Related to Clinical Outcomes Using a Machine Learning Approach.Sensors (Basel, Switzerland) · 2018Trial
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- Vision-Based Assessment of Skeletal Muscle Decline: Correlating Gait Variance with SPPB Performance.Healthcare (Basel, Switzerland) · 2025Article
- Gait Characteristics over the Six-minute Walk Test in Older Women of a Specific Portuguese Community: A Cross-Sectional Study.Current aging science · 2025Article
- Ability of Heart Rate Recovery and Gait Kinetics in a Single Wearable to Predict Frailty: Quasiexperimental Pilot Study.JMIR formative research · 2024Article
- Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors.Sensors (Basel, Switzerland) · 2023Article
- Predictability of Fall Risk Assessments in Community-Dwelling Older Adults: A Scoping Review.Sensors (Basel, Switzerland) · 2023Article
- Comparative Analysis of Fall Risk Assessment Features in Community-Elderly and Stroke Survivors: Insights from Sensor-Based Data.Healthcare (Basel, Switzerland) · 2023Article
- Detection of Fall Risk in Multiple Sclerosis by Gait Analysis-An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms.Brain sciences · 2022Article
- Automated step detection with 6-minute walk test smartphone sensors signals for fall risk classification in lower limb amputees.PLOS digital health · 2022Article
- Amputee Fall Risk Classification Using Machine Learning and Smartphone Sensor Data from 2-Minute and 6-Minute Walk Tests.Sensors (Basel, Switzerland) · 2022Article
- Experimental Verification of Micro-Doppler Radar Measurements of Fall-Risk-Related Gait Differences for Community-Dwelling Elderly Adults.Sensors (Basel, Switzerland) · 2022Article
- Intelligent Fall-Risk Assessment Based on Gait Stability and Symmetry Among Older Adults Using Tri-Axial Accelerometry.Frontiers in bioengineering and biotechnology · 2022Article
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Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Faller classification in elderly populations can facilitate preventative care before a fall occurs. A novel wearable-sensor based faller classification method for the elderly was developed using accelerometer-based features from straight walking and turns. Seventy-six older individuals (74.15 ± 7.0 years), categorized as prospective fallers and non-fallers, completed a six-minute walk test with accelerometers attached to their lower legs and pelvis. After segmenting straight and turn sections, cross validation tests were conducted on straight and turn walking features to assess classification performance. The best "classifier model-feature selector" combination used turn data, random forest classifier, and select-5-best feature selector (73.4% accuracy, 60.5% sensitivity, 82.0% specificity, and 0.44 Matthew's Correlation Coefficient (MCC)). Using only the most frequently occurring features, a feature subset (minimum of anterior-posterior ratio of even/odd harmonics for right shank, standard deviation (SD) of anterior left shank acceleration SD, SD of mean anterior left shank acceleration, maximum of medial-lateral first quartile of Fourier transform (FQFFT) for lower back, maximum of anterior-posterior FQFFT for lower back) achieved better classification results, with 77.3% accuracy, 66.1% sensitivity, 84.7% specificity, and 0.52 MCC score. All classification performance metrics improved when turn data was used for faller classification, compared to straight walking data. Combining turn and straight walking features decreased performance metrics compared to turn features for similar classifier model-feature selector combinations.
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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.