Usability of A Low Fidelity Wearable Device and App to Monitor Covid-19 Out-Patients
Bibliographic record
Abstract
Most wearable healthcare devices facilitate the continuous monitoring of physiological parameters (e.g., electrocardiography and respiratory rate). However, usability issues still arise in many wearable healthcare devices, leaving end-users dissatisfied with the service. To better understand patients' requirements, usability studies and user-centered design research must be conducted. In our study, we aimed to design a wearable health-tracking prototype that is catered for COVID-19 out-patients, we addressed this issue by including stakeholders in our design process. We investigated patients' needs while also understanding crucial metrics from physicians who worked with COVID-19 patients. From our preliminary research and our findings, we designed a wearable health tracking device and health application interface prototype. Then we conducted usability tests to ensure meeting user requirements and satisfaction. According to our system usability scale (SUS) score and other findings, our prototypes were considered usable and acceptable by users. However, due to COVID-19 restrictions, many limitations were placed.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".