Monitoring at-home care patients through a scalar polar plot visualization of motion sensor data
Bibliographic record
Abstract
In Canada, approximately 18 percent (6.6 million) of the total population are age 65 or older, and 88 percent of people over age 65 want to stay in their residence for as long as possible. This older demographic is a group that is dependent on proactive and preventative healthcare. Using motion sensor data collected from a local company providing home-care services to this demographic, a data visualization was constructed to assist users in observing patient behavior and improving their quality of life while maintaining their independence. However, since the collected data is time-based, it results in a dataset that is too large to plot and determine behavior from. The goal of this project was to take an existing polar plot visualization created from the data over a period of one month and scale it to display data over a larger time frame of at least 6 months, allowing us to determine patient behavioral changes over time. This was achieved by using a weighted random sampling method and implemented using Python, Pandas, PlotlyExpress and Dash. As a result of this project, weighted random sampling enabled the viewer to determine behavioral changes and abnormalities using the polar plot visualization over a period of 6 months.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".