ArticleSensors (Basel, Switzerland)2020
A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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
25 citing papers in PubMed.
- Research of Fall Detection and Fall Prevention Technologies: A Review.Sensors (Basel, Switzerland) · 2026Review
- The Impact of the Accelerometer Sampling Rate on the Performance of Machine and Deep Learning Models in Wearable Fall-Detection Systems.Sensors (Basel, Switzerland) · 2025Article
- Interactions during falls with environmental objects: evidence from real-life falls in long-term care captured on video.BMC geriatrics · 2024Article
- Acquisition of Data on Kinematic Responses to Unpredictable Gait Perturbations: Collection and Quality Assurance of Data for Use in Machine Learning Algorithms for (Near-)Fall Detection.Sensors (Basel, Switzerland) · 2024Article
- An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.Journal of medical Internet research · 2024Article
- Wrist-Based Fall Detection: Towards Generalization across Datasets.Sensors (Basel, Switzerland) · 2024Article
- Immediate fall prevention: the missing key to a comprehensive solution for falling hazard in older adults.Frontiers in aging neuroscience · 2024Article
- Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing.Sensors (Basel, Switzerland) · 2023Article
- Stochastic Recognition of Human Physical Activities via Augmented Feature Descriptors and Random Forest Model.Sensors (Basel, Switzerland) · 2022Article
- Fall Detection for Shipboard Seafarers Based on Optimized BlazePose and LSTM.Sensors (Basel, Switzerland) · 2022Article
- A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature.Applied clinical informatics · 2022Review
- A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling.Sensors (Basel, Switzerland) · 2021Article
- Artificial Neural Networks to Solve the Singular Model with Neumann-Robin, Dirichlet and Neumann Boundary Conditions.Sensors (Basel, Switzerland) · 2021Article
- Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
- NT-FDS-A Noise Tolerant Fall Detection System Using Deep Learning on Wearable Devices.Sensors (Basel, Switzerland) · 2021Article
- Fall Detection System-Based Posture-Recognition for Indoor Environments.Journal of imaging · 2021Article
- A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.Sensors (Basel, Switzerland) · 2021Article
- A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors.Frontiers in aging neuroscience · 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.
Funding
Abstract
Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset.
Indexed as
Identifiers
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.