ArticleSensors (Basel, Switzerland)2021
Identifying Fall Risk Predictors by Monitoring Daily Activities at Home Using a Depth Sensor Coupled to Machine Learning Algorithms.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 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.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review.Journal of medical Internet research · 2024Pooled it
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- A systematic review of chiropractic care for fall prevention: rationale, state of the evidence, and recommendations for future research.BMC musculoskeletal disorders · 2022Pooled it
- Artificial intelligence for detecting fall events, assessing fall risks, and preventing fall occurrences in the elderly: A narrative review.Clinical medicine (London, England) · 2026Review
- Understanding Human Motion from Depth Sensors: Activity Recognition and Age Group Recognition Using Skeleton Data.Sensors (Basel, Switzerland) · 2026Article
- Review
- Are We Missing the Environmental Factors in AI-Based Fall Risk Models?: A Systematic Review.Research square · 2026Article
- PilOT-Measure: a mobile 3D depth sensing application to support accurate and efficient clinician-led home-based falls risk assessments.BMC medical informatics and decision making · 2025Article
- Markerless Motion Capture Parameters Associated with Fall Risk or Frailty: A Scoping Review.Sensors (Basel, Switzerland) · 2025Article
- The reliability and validity of a non-wearable indoor positioning system to assess mobility in older adults: A cross-sectional study.PloS one · 2025Article
- Introduction of AI Technology for Objective Physical Function Assessment.Bioengineering (Basel, Switzerland) · 2024Review
- Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.Sensors (Basel, Switzerland) · 2024Article
- Assessing Handrail-Use Behavior during Stair Ascent or Descent Using Ambient Sensing Technology.Sensors (Basel, Switzerland) · 2023Article
- Application of Feedforward and Recurrent Neural Networks for Fusion of Data from Radar and Depth Sensors Applied for Healthcare-Oriented Characterisation of Persons' Gait.Sensors (Basel, Switzerland) · 2023Article
- Methods for Spatiotemporal Analysis of Human Gait Based on Data from Depth Sensors.Sensors (Basel, Switzerland) · 2023Article
- Prediction of Wellness Condition for Community-Dwelling Elderly via ECG Signals Data-Based Feature Construction and Modeling.International journal of environmental research and public health · 2022Article
- A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Because of population ageing, fall prevention represents a human, economic, and social issue. Currently, fall-risk is assessed infrequently, and usually only after the first fall occurrence. Home monitoring could improve fall prevention. Our aim was to monitor daily activities at home in order to identify the behavioral parameters that best discriminate high fall risk from low fall risk individuals. Microsoft Kinect sensors were placed in the room of 30 patients temporarily residing in a rehabilitation center. The sensors captured the patients' movements while they were going about their daily activities. Different behavioral parameters, such as speed to sit down, gait speed or total sitting time were extracted and analyzed combining statistical and machine learning algorithms. Our algorithms classified the patients according to their estimated fall risk. The automatic fall risk assessment performed by the algorithms was then benchmarked against fall risk assessments performed by clinicians using the Tinetti test and the Timed Up and Go test. Step length, sit-stand transition and total sitting time were the most discriminant parameters to classify patients according to their fall risk. Coupling step length to the speed required to stand up or the total sitting time gave rise to an error-less classification of the patients, i.e., to the same classification as that of the clinicians. A monitoring system extracting step length and sit-stand transitions at home could complement the clinicians' assessment toolkit and improve fall prevention.
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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.