Trial reportPloS one2016
Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection.
Trial report in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 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
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Smartphones and Threshold-Based Monitoring Methods Effectively Detect Falls Remotely: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- A Systematic Review of Wearable Sensors for Monitoring Physical Activity.Sensors (Basel, Switzerland) · 2022Pooled it
- How Algorithmic Technologies 'Constitute' the Older Body: A Study of Fall-Detection Wearables.Sociology of health & illness · 2026Article
- Efficient Fall Detection from Wrist-Worn IMU Signals via Knowledge Distillation: A Lightweight CNN Approach Using the UMAFall Dataset.Sensors (Basel, Switzerland) · 2026Article
- A Comparative Overview of Technological Advances in Fall Detection Systems for Elderly People.Sensors (Basel, Switzerland) · 2025Article
- Wrist-Based Fall Detection: Towards Generalization across Datasets.Sensors (Basel, Switzerland) · 2024Article
- Deep Wavelet Convolutional Neural Networks for Multimodal Human Activity Recognition Using Wearable Inertial Sensors.Sensors (Basel, Switzerland) · 2023Article
- Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing.Sensors (Basel, Switzerland) · 2023Article
- A Class-Imbalanced Deep Learning Fall Detection Algorithm Using Wearable Sensors.Sensors (Basel, Switzerland) · 2021Article
- Article
- A Feasibility Study of the Use of Smartwatches in Wearable Fall Detection Systems.Sensors (Basel, Switzerland) · 2021Article
- Usability of a Fall Risk mHealth App for People With Multiple Sclerosis: Mixed Methods Study.JMIR human factors · 2021Article
- A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.Sensors (Basel, Switzerland) · 2020Article
- On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems.Journal of healthcare engineering · 2020Article
- Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning.Sensors (Basel, Switzerland) · 2018Article
- Analysis of Public Datasets for Wearable Fall Detection Systems.Sensors (Basel, Switzerland) · 2017Review
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
During the last years, many research efforts have been devoted to the definition of Fall Detection Systems (FDSs) that benefit from the inherent computing, communication and sensing capabilities of smartphones. However, employing a smartphone as the unique sensor in a FDS application entails several disadvantages as long as an accurate characterization of the patient's mobility may force to transport this personal device on an unnatural position. This paper presents a smartphone-based architecture for the automatic detection of falls. The system incorporates a set of small sensing motes that can communicate with the smartphone to help in the fall detection decision. The deployed architecture is systematically evaluated in a testbed with experimental users in order to determine the number and positions of the sensors that optimize the effectiveness of the FDS, as well as to assess the most convenient role of the smartphone in the architecture.
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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.