ReviewSensors (Basel, Switzerland)2017
Analysis of Public Datasets for Wearable Fall Detection Systems.
Review in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Privacy-Preserving Detection of Post-Fall Lying Posture Using a Low-Resolution Infrared Sensor and an Edge FOMO Neural Network.Sensors (Basel, Switzerland) · 2026Article
- 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
- Wrist-Based Fall Detection: Towards Generalization across Datasets.Sensors (Basel, Switzerland) · 2024Article
- An Enhanced Ensemble Deep Neural Network Approach for Elderly Fall Detection System Based on Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Identification of the cause of fall during the pre-impact fall period.Journal of physical therapy science · 2022Article
- RETRACTED: FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems.Sensors (Basel, Switzerland) · 2022Article
- Fall Risk Assessment Using Wearable Sensors: A Narrative Review.Sensors (Basel, Switzerland) · 2022Review
- Machine Learning for Healthcare Wearable Devices: The Big Picture.Journal of healthcare engineering · 2022Review
- Machine-Learning-Based Human Fall Detection Using Contact- and Noncontact-Based Sensors.Computational intelligence and neuroscience · 2022Article
- A machine learning based sentient multimedia framework to increase safety at work.Multimedia tools and applications · 2022Article
- Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults.Sensors (Basel, Switzerland) · 2021Article
- Smart Wearables with Sensor Fusion for Fall Detection in Firefighting.Sensors (Basel, Switzerland) · 2021Article
- Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 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
- Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls.Sensors (Basel, Switzerland) · 2020Article
- A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets.Sensors (Basel, Switzerland) · 2020Article
- A Communication Infrastructure for the Health and Social Care Internet of Things: Proof-of-Concept Study.JMIR medical informatics · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the boom of wireless handheld devices such as smartwatches and smartphones, wearable Fall Detection Systems (FDSs) have become a major focus of attention among the research community during the last years. The effectiveness of a wearable FDS must be contrasted against a wide variety of measurements obtained from inertial sensors during the occurrence of falls and Activities of Daily Living (ADLs). In this regard, the access to public databases constitutes the basis for an open and systematic assessment of fall detection techniques. This paper reviews and appraises twelve existing available data repositories containing measurements of ADLs and emulated falls envisaged for the evaluation of fall detection algorithms in wearable FDSs. The analysis of the found datasets is performed in a comprehensive way, taking into account the multiple factors involved in the definition of the testbeds deployed for the generation of the mobility samples. The study of the traces brings to light the lack of a common experimental benchmarking procedure and, consequently, the large heterogeneity of the datasets from a number of perspectives (length and number of samples, typology of the emulated falls and ADLs, characteristics of the test subjects, features and positions of the sensors, etc.). Concerning this, the statistical analysis of the samples reveals the impact of the sensor range on the reliability of the traces. In addition, the study evidences the importance of the selection of the ADLs and the need of categorizing the ADLs depending on the intensity of the movements in order to evaluate the capability of a certain detection algorithm to discriminate falls from ADLs.
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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.