Developing social robots for aging populations: A literature review of recent academic sources
Bibliographic record
Abstract
Abstract The perception of aging populations is a major factor driving the social robot development movement. A growing body of research reflects the expanding interest in social robots. This paper synthesizes research on the development of social robots with a literature review of academic articles with publication dates ranging from 2006 to 2017. The review is divided into three themes: (a) robots as an aid in treatment; (b) robots as social assistants and home companions; and (c) robots as custodial caregivers that are viewed in terms of ethical implications. This paper outlines the issues surrounding social, commitment, assistive, and companion robots for use in medical treatment, mental health therapy, physiotherapy, care facilities, and private homes. It describes some of the ethical concerns raised by researchers and media, including questions of control, privacy, consent, and the issue of simulated versus human compassion in caregiving. The research also points out that a rhetoric of urgency concerning aging populations drives the development of robots, which frames citizens who will benefit from robots in reductive ways. We argue that the contribution of humanities and social science research, including age studies and critical gerontology, should be better integrated with discourses of social robot development, largely from technical fields.
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 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".