Advances in applying somatosensory interaction technology in geriatric care: A bibliometric analysis
Bibliographic record
Abstract
Objectives: Somatosensory Interaction Technology (SIT) is used in various aspects of geriatric care. We aimed to conduct a bibliometric analysis to summarize relevant publications and visualize publication characteristics, current hotspots, and development trends, thereby inspiring subsequent researches. Methods: We searched the Web of Science Core Collection database for publications on the application of SIT in geriatric care. Bibliometric visualization and clustering analysis were performed using VOSviewer V1.6.18 Software, while keywords burst detection analysis was conducted with CiteSpace 6.1.R6 Software. Results: After screening, a total of 1,019 publications were included. The number of publications on SIT in geriatric care is gradually increasing, exhibiting a rapid growth rate. The United States, Canada, and Australia led in terms of publication volume. Keyword clustering analysis identified major research hotspots: crisis warning, somatic abilities, rehabilitation training and psychosocial support. Initial studies primarily explored themes such as recovery, movement, systems, and later shifted towards gait analysis, muscle strength, parameters, and home-based care. More recently, research themes have evolved to dementia, machine learning, and gamification. Conclusions: SIT is innovative for promoting active aging, advancing intelligent healthcare, and elevating the daily quality of life for older adults in clinical and domestic settings. Applications of SIT can be categorized into early warning systems for crises, detailed analyses of physical conditions, rehabilitation enhancement, and support for psychosocial health. Research trends have transitioned from whole-body recognition to precise feedback, from a focus on physical health to mental health, and from technical feasibility to user-friendliness. Future research should focus on developing accessible and user-friendly devices, fostering interdisciplinary collaborations for innovation, expanding research to address both the physical and mental health needs of diverse older adults, and integrating emerging technologies to enhance data precision and accelerate the development of intelligent platforms.
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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 | 0.023 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.178 | 0.194 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".