<i>MiVitals</i> – <b> <i>Mi</i> </b> xed Reality Interface for <b> <i>Vitals</i> </b> Monitoring: A HoloLens based prototype for healthcare practices
Bibliographic record
Abstract
In this paper, we introduce MiVitals —a Mixed Reality (MR) system designed for healthcare professionals to monitor patients in wards or clinics. We detail the design, development, and evaluation of MiVitals , which integrates real-time vital signs from a biosensor-equipped wearable, Vitaliti TM . The system generates holographic visualizations, allowing healthcare professionals to interact with medical charts and information panels holographically. These visualizations display vital signs, trends, other significant physiological signals, and medical early warning scores in a comprehensive manner. We conducted a User Interface/User Experience (UI/UX) study focusing on novel holographic visualizations and interfaces that intuitively present medical information. This approach brings traditional bedside medical information to life in the real environment through non-contact 3D images, supporting rapid decision-making, vital pattern and anomaly detection, and enhancing clinicians' performance in wards. Additionally, we present findings from a usability study involving medical doctors and healthcare practitioners to assess MiVitals ' efficacy. The System Usability Scale study yielded a score of 84, indicating that the MiVitals system has high usability.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".