Differentiating assisted and unassisted bed exits using ultrasonic sensor
Bibliographic record
Abstract
Monitoring bed exits is critical for establishing the mobility trend of a person. Sensors such as pressure sensitive mats have been used to monitor symmetry and timing of a sit to stand transfer to establish a mobility trend. In an uncontrolled setting, the timing and symmetry measurements for sit-to-stand transfers may be affected by friends, family and caregivers providing assistance during such transfer. It is of significant importance to differentiate assisted and unassisted transfers in order to accurately establish a mobility trend. This paper will use unobtrusive ultrasound sensors in order to monitor the presence of another person during the sit-to-stand transition. This paper will examine different sensor placements in order to optimize the detection area and minimize the undetected cases. A lab simulation was performed in a controlled setting comparing the different configurations. The optimal solution was verified in a hospital setting to maximize the detection of assisted vs. unassisted transfers. The results show an 86.2% coverage of the hospital room and an 80% successful detection rate of a third party. This allows for detection of assisted sit-to-stand transfer if the third party is present within this coverage area. We also demonstrate how passive infrared sensors (PIR) can be integrated in the proposed system to enhance room entry and exit detection.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".