Evidence map›Paper›PMID 36080971›Full record

ArticleSensors (Basel, Switzerland)2022

Handheld Device-Based Indoor Localization with Zero Infrastructure (HDIZI).

Abdullah M AlSahly, Mohammad Mehedi Hassan, Kashif Saleem, Amerah Alabrah, Joel J P C Rodrigues

Abstract read
In one paragraph

Article 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Smartphone Sensors for Indoor Positioning.Sensors (Basel, Switzerland) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdullah M AlSahlyDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.ORCID 0000-0002-4321-1137
Mohammad Mehedi HassanDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.ORCID 0000-0002-3479-3606
Kashif SaleemCenter of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11653, Saudi Arabia.ORCID 0000-0001-8062-3301
Amerah AlabrahDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Joel J P C RodriguesCollege of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266555, China.ORCID 0000-0001-8657-3800

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The correlations between smartphone sensors, algorithms, and relevant techniques are major components facilitating indoor localization and tracking in the absence of communication and localization standards. A major research gap can be noted in terms of explaining the connections between these components to clarify the impacts and issues of models meant for indoor localization and tracking. In this paper, we comprehensively study the smartphone sensors, algorithms, and techniques that can support indoor localization and tracking without the need for any additional hardware or specific infrastructure. Reviews and comparisons detail the strengths and limitations of each component, following which we propose a handheld-device-based indoor localization with zero infrastructure (HDIZI) approach to connect the abovementioned components in a balanced manner. The sensors are the input source, while the algorithms are used as engines in an optimal manner, in order to produce a robust localizing and tracking model without requiring any further infrastructure. The proposed framework makes indoor and outdoor navigation more user-friendly, and is cost-effective for researchers working with embedded sensors in handheld devices, enabling technologies for Industry 4.0 and beyond. We conducted experiments using data collected from two different sites with five smartphones as an initial work. The data were sampled at 10 Hz for a duration of five seconds at fixed locations; furthermore, data were also collected while moving, allowing for analysis based on user stepping behavior and speed across multiple paths. We leveraged the capabilities of smartphones, through efficient implementation and the optimal integration of algorithms, in order to overcome the inherent limitations. Hence, the proposed HDIZI is expected to outperform approaches proposed in previous studies, helping researchers to deal with sensors for the purposes of indoor navigation-in terms of either positioning or tracking-for use in various fields, such as healthcare, transportation, environmental monitoring, or disaster situations.

Indexed as

AlgorithmsSmartphoneComputersTransportationblueprintfilter algorithmindoor localizationindoor trackingmachine learningmultisensor data fusionsmartphone sensorvirtual IMUWeb of Things

Identifiers

PMID36080971
PMCPMC9460854

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.