ArticleSensors (Basel, Switzerland)2018
Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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Who cites it
10 citing papers in PubMed.
- Gyro-Mag: Attack-Resilient System Based on Sensor Estimation.Sensors (Basel, Switzerland) · 2025Article
- Sudden Fall Detection of Human Body Using Transformer Model.Sensors (Basel, Switzerland) · 2024Article
- Analyzing Optimal Wearable Motion Sensor Placement for Accurate Classification of Fall Directions.Sensors (Basel, Switzerland) · 2024Article
- Wrist-Based Fall Detection: Towards Generalization across Datasets.Sensors (Basel, Switzerland) · 2024Article
- A Survey of Human Gait-Based Artificial Intelligence Applications.Frontiers in robotics and AI · 2021Review
- Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring.Journal of healthcare informatics research · 2021Article
- Posture and Physical Activity Detection: Impact of Number of Sensors and Feature Type.Medicine and science in sports and exercise · 2020Article
- On the Heterogeneity of Existing Repositories of Movements Intended for the Evaluation of Fall Detection Systems.Journal of healthcare engineering · 2020Article
- Accelerometer-Based Human Fall Detection Using Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2019Article
- SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA).
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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.