Robotic Companions for Assisted Living and to Age Well
Bibliographic record
Abstract
As the global population ages, the demand for assistive technologies, particularly robotic companions, has grown significantly. This chapter explores the development, categorization, and application of socially assistive robots in assisted living, focusing on their role in enhancing the physical and emotional well-being of elderly individuals. The chapter begins with an introduction to the history of robotics, highlighting key technological advancements that have shaped the current landscape of robotic companions. It then categorizes these robots into pet-like, humanoid, and telepresence designs, each offering unique benefits for assisted living. Several well-known robots, including Paro, Pepper, and Nao, are analyzed for their effectiveness in supporting older adults through companionship, cognitive stimulation, and physical assistance. Ethical considerations and challenges, such as data privacy, emotional attachment, and affordability, are also discussed. Ultimately, this chapter aims to provide a comprehensive overview of the role of robotic companions in improving the quality of life for the elderly, while emphasizing the importance of balancing technological innovation with ethical responsibility in the development of these assistive technologies.
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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".