ReviewSensors (Basel, Switzerland)2018
Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 41 papers, 7 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- The Effects of Vibratory and Acoustic Stimulations on Postural Control in Healthy People: A Systematic Review.Annals of biomedical engineering · 2023Pooled it
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- Are wearable devices effective for preventing and detecting falls: an umbrella review (a review of systematic reviews).BMC public health · 2021Pooled it
- Performance and Characteristics of Wearable Sensor Systems Discriminating and Classifying Older Adults According to Fall Risk: A Systematic Review.Sensors (Basel, Switzerland) · 2021Pooled it
- eHealth for Addressing Balance Disorders in the Elderly: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Effects of Wearable Devices with Biofeedback on Biomechanical Performance of Running-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
- Fall prevention interventions in primary care to reduce fractures and falls in people aged 70 years and over: the PreFIT three-arm cluster RCT.Health technology assessment (Winchester, England) · 2021Trial
- Development of a Strategy to Predict and Detect Falls Using Wearable Sensors.Journal of medical systems · 2019Trial
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- Performance Analysis of Data Augmentation Approaches for Improving Wrist-Based Fall Detection System.Sensors (Basel, Switzerland) · 2025Article
- Opportunities to Digitally Enable Falls Prevention in Older Adults.The Gerontologist · 2025Review
- Sensor-Based Gait and Balance Assessment in Healthy Adults: Analysis of Short-Term Training and Sensor Placement Effects.Sensors (Basel, Switzerland) · 2024Article
- Wearable Technologies for Healthy Ageing: Prospects, Challenges, and Ethical Considerations.The Journal of frailty & aging · 2024Article
- Clinical Sensor-Based Fall Risk Assessment at an Orthopedic Clinic: A Case Study of the Staff's Views on Utility and Effectiveness.Sensors (Basel, Switzerland) · 2023Review
- Machine learning empowered COVID-19 patient monitoring using non-contact sensing: An extensive review.Journal of pharmaceutical analysis · 2022Review
- Smart Eyeglasses: A Valid and Reliable Device to Assess Spatiotemporal Parameters during Gait.Sensors (Basel, Switzerland) · 2022Article
- Remote Healthcare for Elderly People Using Wearables: A Review.Biosensors · 2022Review
- Fall Risk Assessment Using Wearable Sensors: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Detecting Fall Risk and Frailty in Elders with Inertial Motion Sensors: A Survey of Significant Gait Parameters.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, the meaning of successful living has moved from extending lifetime to improving the quality of aging, mainly in terms of high cognitive and physical functioning together with avoiding diseases. In healthy elderly, falls represent an alarming accident both in terms of number of events and the consequent decrease in the quality of life. Stability control is a key approach for studying the genesis of falls, for detecting the event and trying to develop methodologies to prevent it. Wearable sensors have proved to be very useful in monitoring and analyzing the stability of subjects. Within this manuscript, a review of the approaches proposed in the literature for fall risk assessment, fall prevention and fall detection in healthy elderly is provided. The review has been carried out by using the most adopted publication databases and by defining a search strategy based on keywords and boolean algebra constructs. The analysis aims at evaluating the state of the art of such kind of monitoring, both in terms of most adopted sensor technologies and of their location on the human body. The review has been extended to both dynamic and static analyses. In order to provide a useful tool for researchers involved in this field, the manuscript also focuses on the tests conducted in the analyzed studies, mainly in terms of characteristics of the population involved and of the tasks used. Finally, the main trends related to sensor typology, sensor location and tasks have been identified.
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What OpenQuestion 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 OpenQuestion graph.