Evidence map›Paper›PMID 41359955›Full record

ArticleJMIR research protocols2025

Use of Head-Mounted Inertial Sensors for Mobility Tasks: Protocol for a Scoping Review.

Will Sloan, Grace Elizabeth MacDonald, R Bruce Wallace, Rafik Goubran, Heidi Sveistrup

Abstract read
In one paragraph

Article in JMIR research protocols, 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Will SloanDepartment of Systems and Computer Engineering, Faculty of Engineering, Carleton University, Ottawa, ON, Canada.ORCID 0000-0002-4644-1675
Grace Elizabeth MacDonaldDepartment of Systems and Computer Engineering, Faculty of Engineering, Carleton University, Ottawa, ON, Canada.ORCID 0009-0001-1738-9054
R Bruce WallaceDepartment of Systems and Computer Engineering, Faculty of Engineering, Carleton University, Ottawa, ON, Canada.ORCID 0000-0003-4379-2717
Rafik GoubranDepartment of Systems and Computer Engineering, Faculty of Engineering, Carleton University, Ottawa, ON, Canada.ORCID 0000-0003-4087-416X
Heidi SveistrupDepartment of Systems and Computer Engineering, Faculty of Engineering, Carleton University, Ottawa, ON, Canada.ORCID 0000-0002-7335-8121

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHuman activity recognition (HAR) is the use of technology to detect a person's movements. Sensors can be passive like cameras monitoring an area, or active like those attached to a person's watch. HAR is a burgeoning field whose uses span from personal health tracking to at-home physical degradation monitoring. The benefit of having sensors attached to a person for constant HAR tracking can be seen in the personal fitness field where people track their number of steps or distance ran. For older adults, HAR can be used in combination with mobility tasks like sit-to-stand, timed-up-and-go, and other tasks to monitor a person's ability to be self-sufficient and to live alone at home. Together, HAR and mobility tasks are an effective method to measure health, especially in older adults. Consumer wearables like Fitbits and Apple watches are currently being used to measure many of these mobility tasks. The wide adoption of these devices is a result of their ease of use and simplicity. Despite this, most wearables go unused shortly after purchase. A potential solution is the use of consumer devices that are already adopted like headphones or hearing aids. These devices can perform the same measurements as the wearables but have the advantage of being used for other reasons than health monitoring. Despite this, no scoping review has summarized the current capabilities of what can be done using head-worn sensors.

objectiveWe aim to understand how head-worn devices have been used to monitor dynamic mobility tasks and if they have been compared to traditional methods (like smartwatches or in-sole sensors).

methodsThis scoping review will be guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. There is no time limit to when the papers must have been published. The databases included in the search are PubMed, Engineering Village, SCOPUS, and Web of Science. Using pre-established inclusion and exclusion criteria, 2 reviewers will screen each paper. We will follow 3 stages of the screening process: abstract and title screening, full-text screening, and full-text data extraction.

resultsThe search will be started once this scoping review protocol is published.

conclusionsHead-mounted devices have become part of everyday life and many already contain accelerometers or inertial measurement units. This scoping review will examine how these sensors have already been used in research to measure mobility tasks, and determine how consumer devices can be adapted to improve the lives of regular people. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/67628.

Indexed as

AccelerometryWearable Electronic DevicesHeadHumansMovementResearch DesignScoping Reviews as Topicaccelerometerhuman activity recognitioninertial measurement unitinertial sensormobilitysensorwearable device, scoping review

Identifiers

PMID41359955
PMCPMC12723364

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Registered trials

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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.