A BLE based turnkey indoor positioning system for mobility assessment in aging-in-place settings.

Haixin Wang, Guha Ganesh, Michael Zon, Oishee Ghosh, Henry Siu, Qiyin Fang

Abstract read

In one paragraph

Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

6 authors.

Haixin WangSchool of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.

Guha GaneshSchool of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.

Michael ZonSchool of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.

Oishee GhoshSchool of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.

Henry SiuDepartment of Family Medicine, McMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0002-1593-6827

Qiyin FangSchool of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.ORCID https://orcid.org/0000-0003-2786-9884

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Indoor positioning systems (IPS) can be used to measure mobility at home, which is an important indicator for health and wellbeing. In this work, we designed and developed a Bluetooth Low Energy (BLE) based IPS that identifies individual users; does not require floorplans; and allows the end-users to perform on-site install/setup. Additionally, a dynamic calibration process is implemented to learn room boundaries based on the distribution of the BLE signal strength. The functionality and performance of IPS system were validated in two residential home settings. Raw and filtered relative signal strength indicators (RSSI) and variability of RSSI were measured during testing. Room detection was determined by comparing a user input location (ground truth) with the IPS detected location for over 300 positions. The IPS produced a 96% accuracy of correctly detecting room location when using RSSI and the additional motion sensors. The use of PIR motion and ultrasonic sensors information provided improved validity when compared with existing indoor positioning systems. The ease of use and modular design of this IPS makes it a good choice for implementation in larger scale smart healthcare monitoring systems.

Identifiers

PMID40244932

DOI10.1371/journal.pdig.0000774

PMCPMC12005499

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LicenceCC BY

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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.