Advances in Applying Somatosensory Interaction Technology in Geriatric Health Management: Bibliometric Analysis (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Background: Geriatric health management is rapidly developing as the number of older people living with diseases is increasing globally. The current main health management problems of elderly people include the lack of timely information feedback and standardized information collection and continuous monitoring, which make it difficult to establish timely tracking and retrieval of health data. Somatosensory interaction technology (SIT) enables more direct communication and interaction between the device and the surrounding environment, meaning that it can play a role in the field of geriatric health management. </sec> <sec> <title>OBJECTIVE</title> Objective: The purpose of this study was to summarize the current applications of SIT in geriatric health management, analyze the present and future research hotspots, and provide references for researchers in this field. </sec> <sec> <title>METHODS</title> Methods: We searched the Web of Science Core Collection database for literature on “somatosensory interaction technology” and “geriatric health management.” VOSviewer 1.6.18 and CiteSpace 6.1.R6 software were used to perform the bibliometric visualization and clustering, including the number of articles, countries, institutions, authors, references, and keywords. </sec> <sec> <title>RESULTS</title> Results: In total, 1019 documents were included after screening, the number of publications on SIT in geriatric health management is gradually increasing, and the growth rate is accelerating. The top three countries in terms of the number of publications were the United States (n=275), Canada (n=90), and Australia (n=72). The top three institutions in terms of the number of publications were the University of California in the US (n=30), Tel Aviv University in Israel (n=28), and the University of Toronto in Canada (n=24). The top three most prolific authors were Jeffrey, Hausdorff in Israel (n=13), Jaarsma, Tiny (n=12) and Stromberg, Anna (n=12) in Sweden. A few high-level comprehensive universities and prolific authors lead most of the research. Their collaborations are characterized by a concentration in the same country but global fragmentation. Keyword clustering revealed that research directions were clustered around “risk assessment,” “somatic abilities,” “rehabilitation training,” and “mental health promotion.” Research hotspots of recent years included “machine learning,” “games,” and “dementia.” </sec> <sec> <title>CONCLUSIONS</title> Conclusions: Publications in the field have been increasing at an accelerated rate. However, the increase in core publications is mainly concentrated in individual developed countries and individual authors. There must be greater cross-country and cross-population promotion. SIT can be applied in the risk assessment, somatic abilities, rehabilitation training, and mental health promotion of elderly people. In the future, more in-depth studies in conjunction with new technologies are needed to explore mental health and real-time risk feedback for older people. </sec> <sec> <title>CLINICALTRIAL</title> Not applicable. </sec>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.400 | 0.284 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".