Challenges and opportunities in sensor-based fall prevention for older adults: a bibliometric review
Bibliographic record
Abstract
Purpose This bibliometric review examines the recent literature on sensor-based fall prevention for older adults. It analyzes publication trends, key researchers and institutions, major research themes, as well as gaps and opportunities in this field. Design/methodology/approach A comprehensive search was conducted in Scopus and Web of Science (WoS) databases for publications from 1990 to 2024. Bibliometric indicators including publication output, citation analysis and co-occurrence of keywords were used to map the research landscape. Network visualizations were employed to identify key thematic clusters. Findings The research on sensor-based fall prevention has grown rapidly, peaking in 2019. The USA, Australia and Canada lead this work, with universities and hospitals collaborating globally. Key themes include fall epidemiology, wearable sensors and AI for fall detection. Opportunities exist to better implement these sensor systems through large trials, user-centered design, hybrid sensors and advanced analytics. Research limitations/implications While comprehensive, the analysis focused primarily on publications indexed in Scopus and WoS, which may not capture all relevant literature. Future studies could expand the search to include other databases and conduct deeper analyses of highly influential studies. Practical implications The review provides an evidence-informed roadmap to accelerate the translation of sensor innovations into scalable and sustainable fall prevention practices for vulnerable older adult populations. Originality/value This is the first comprehensive bibliometric analysis to map the research landscape of sensor-based fall prevention, identifying key trends, themes and opportunities to advance this critical domain addressing a major global public health challenge.
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 | 0.030 | 0.159 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.142 | 0.146 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".