Can I be of Assistance?: Socially Assistive Robots as the Next Generation of Health Care Helpers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Now more than ever, robots are seen as a unique strategic technology that will become an important part of society. One main motivation for incorporating intelligent robots into society is our increasing elderly population. Globally we are facing severe demographic challenges due to a low population growth rate coupled with an aging population. This scenario is quickly worsening as baby boomers are beginning to retire, increasing the demands put on health care professionals. This talk will present some of our recent research efforts in developing intelligent assistive robots for the elderly and their integration into health monitoring, and social and cognitive interventions. The ability of such robots to autonomously provide cognitive and social stimuli, guidance, and support, and serve as general assistance to individuals as well as groups of users will be discussed. Socially assistive robots can assist in therapeutic interventions and provide assistance with activities of daily living for people suffering from cognitive impairments, and they can also aid in preventing depression and improving vital signs via their social interaction capabilities. Studies conducted during human-robot interaction scenarios with our autonomous human-like assistive robots Brian, Tangy and Casper will also be discussed.
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.
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 | 0.000 |
| 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 it