SynthesisSensors (Basel, Switzerland)2022
A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.
Synthesis in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06508892 (Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts), which is not on this map. Cited by 41 papers, 8 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Using Consumer-grade Wearable Devices for Fall Risk Evaluation and Alerts
Who cites it
41 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Accuracy of wearable devices in predicting falls in older adults: a systematic review and meta-analysis.Frontiers in public health · 2026Pooled it
- 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
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.JMIR mHealth and uHealth · 2024Pooled it
- Performance of digital technologies in assessing fall risks among older adults with cognitive impairment: a systematic review.GeroScience · 2024Pooled it
- Effectiveness of sensor-based interventions in improving gait and balance performance in older adults: systematic review and meta-analysis of randomized controlled trials.Journal of neuroengineering and rehabilitation · 2024Pooled it
- Digital technologies to prevent falls in people living with dementia or mild cognitive impairment: a rapid systematic overview of systematic reviews.Age and ageing · 2024Pooled it
- The Application of Wearable Sensors and Machine Learning Algorithms in Rehabilitation Training: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Technology-Enhanced Health Care in Smart Homes: Scoping Review of Sensor Technologies, Clinical Applications, Integration Challenges, and Future Directions.JMIR medical informatics · 2026Article
- Electrical bioimpedance spectral markers distinguish balance ability in older adults without provoking fall risk.Journal of neuroengineering and rehabilitation · 2026Article
- Wearable Assessment of Dynamic Trunk Sway Reveals Directional Balance Adaptations During Sling-Assisted Walking After Stroke.Journal of clinical medicine · 2026Article
- RDD-YOLO: a real-time fall detection model incorporating a lightweight visual transformer with dynamic sampling.Scientific reports · 2026Article
- Perceptions of care home residents, families and staff about wearable devices: a scoping review.European geriatric medicine · 2026Article
- Quantitative assessment of the 30-second sit-to-stand test using computer vision technology for physical therapists.Journal of neuroengineering and rehabilitation · 2026Article
- Artificial intelligence in geriatric healthcare: a scoping review.BMC geriatrics · 2026Article
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- Benchmarking Time-Series Artificial Intelligence Architectures for Wearable Sensor-Based Fall Prediction: A Synthetic Data Simulation Framework.Sensors (Basel, Switzerland) · 2026Article
- Comparative analysis of wearable-derived gait features with intrinsic risk indicators for fall risk prediction in older adults.Scientific reports · 2026Article
- Can urban renewal improve the self-assessed health of older adults? Evidence from China.International journal for equity in health · 2026Article
- A comparison of sensor-based lower extremity reaction times, walking speeds, functional performance, and activity fear in older adults with a history of falls: a cross-sectional study.BMC geriatrics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Falls have been recognized as the major cause of accidental death and injury in people aged 65 and above. The timely prediction of fall risks can help identify older adults prone to falls and implement preventive interventions. Recent advancements in wearable sensor-based technologies and big data analysis have spurred the development of accurate, affordable, and easy-to-use approaches to fall risk assessment. The objective of this study was to systematically assess the current state of wearable sensor-based technologies for fall risk assessment among community-dwelling older adults. Twenty-five of 614 identified research articles were included in this review. A comprehensive comparison was conducted to evaluate these approaches from several perspectives. In general, these approaches provide an accurate and effective surrogate for fall risk assessment. The accuracy of fall risk prediction can be influenced by various factors such as sensor location, sensor type, features utilized, and data processing and modeling techniques. Features constructed from the raw signals are essential for predictive model development. However, more investigations are needed to identify distinct, clinically interpretable features and develop a general framework for fall risk assessment based on the integration of sensor technologies and data modeling.
Indexed as
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What Socratic holds
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 Socratic graph.