ReviewSensors (Basel, Switzerland)2022
Comprehensive Analysis of Applied Machine Learning in Indoor Positioning Based on Wi-Fi: An Extended Systematic Review.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- A Review of Indoor Localization Methods Leveraging Smartphone Sensors and Spatial Context.Sensors (Basel, Switzerland) · 2024Review
- Few-Shot Learning in Wi-Fi-Based Indoor Positioning.Biomimetics (Basel, Switzerland) · 2024Article
- Ada-LT IP: Functional Discriminant Analysis of Feature Extraction for Adaptive Long-Term Wi-Fi Indoor Localization in Evolving Environments.Sensors (Basel, Switzerland) · 2024Article
- A Selection of Starting Points for Iterative Position Estimation Algorithms Using Feedforward Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Human Walking Direction Detection Using Wireless Signals, Machine and Deep Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- Indoor Localization Algorithm Based on a High-Order Graph Neural Network.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Assists IoT Localization: A Review of Current Challenges and Future Trends.Sensors (Basel, Switzerland) · 2023Review
- Improving Fingerprint-Based Positioning by Using IEEE 802.11mc FTM/RTT Observables.Sensors (Basel, Switzerland) · 2022Article
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
Nowadays, there are a multitude of solutions for indoor positioning, as opposed to standards for outdoor positioning such as GPS. Among the different existing studies on indoor positioning, the use of Wi-Fi signals together with Machine Learning algorithms is one of the most important, as it takes advantage of the current deployment of Wi-Fi networks and the increase in the computing power of computers. Thanks to this, the number of articles published in recent years has been increasing. This fact makes a review necessary in order to understand the current state of this field and to classify different parameters that are very useful for future studies. What are the most widely used machine learning techniques? In what situations have they been tested? How accurate are they? Have datasets been properly used? What type of Wi-Fi signals have been used? These and other questions are answered in this analysis, in which 119 papers are analyzed in depth following PRISMA guidelines.
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.