Use of Head-Mounted Inertial Sensors for Mobility Tasks: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Human 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. OBJECTIVE: We 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). METHODS: This 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. RESULTS: The search will be started once this scoping review protocol is published. CONCLUSIONS: Head-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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".