Leveraging Simulation and Virtual Reality for a Long Term Care Facility Service Robot During COVID-19
Bibliographic record
Abstract
Providing care to seniors and adults with Developmental Disabilities (DD) presents challenges associated with care, companionship, medication intake, and fall monitoring among others. Currently, measures to prevent the spread of COVID-19 have seen restricted access to those living in long-term care facilities (LTCFs). While technologies such as robotics and virtual reality (VR) have seen advances in overcoming the aforementioned challenges, the restrictions have impacted research and development relying on human participants. Recently, the use of synthetic data for training motion detection algorithms and virtual worlds has been gaining momentum as an alternative continue for simulating robot and human interactions instead of relying on public databases and physical locations. Here, we propose the development of VR robot simulator for Aether™, a socially assistive mobile robot created to help seniors and people living with DD to achieve a higher degree of independence. For example, Aether™can assist caregivers by alleviating the burden of care by monitoring the LTCF for tripping hazards, open doors and cabinets. Our simulator allows configuring the virtual Aether™robot to navigate a virtual environment and detect upper limb gestures performed by a virtual avatar. Our preliminary results indicate that the virtual sensor has detection equivalent to the real sensor, thus ensuring that the simulated data is transferable for real-world testing.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".