Can I be of Assistance?: Socially Assistive Robots as the Next Generation of Health Care Helpers
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.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".