ArticleSensors (Basel, Switzerland)2021
A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Wearable airbag technology and machine learned models to mitigate falls after stroke.Journal of neuroengineering and rehabilitation · 2022Trial
- Daily fall risk assessment for older adults using a balance sensor with machine learning.Frontiers in public health · 2026Article
- 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
- Article
- Sensor Networks: Physical and Social Sensing in the IoT.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning Multi-Class Approach for Human Fall Detection Based on Doppler Signatures.International journal of environmental research and public health · 2023Article
- IoT-blockchain empowered Trinet: optimized fall detection system for elderly safety.Frontiers in bioengineering and biotechnology · 2023Article
- Detection of Fall Risk in Multiple Sclerosis by Gait Analysis-An Innovative Approach Using Feature Selection Ensemble and Machine Learning Algorithms.Brain sciences · 2022Article
- A Deep Convolutional Neural Network-XGB for Direction and Severity Aware Fall Detection and Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling.Sensors (Basel, Switzerland) · 2021Article
- Influence of the Antenna Orientation on WiFi-Based Fall Detection Systems.Sensors (Basel, Switzerland) · 2021Article
- Event-Centered Data Segmentation in Accelerometer-Based Fall Detection Algorithms.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from
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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.