An Integrated Social Robot and Virtual Assistant Solution to Support Medical Management for Older Adults
Bibliographic record
Abstract
ABSTRACT Introduction The global aging population leads to increased demand for professional caregivers and innovative assistive technologies. Traditional aids such as canes and hearing devices have long supported older adults, but emerging solutions involving robotics and AI open new opportunities for enhanced care and independence. Objectives This study aimed to design and evaluate an assistive solution that integrates a social robot and a virtual assistant to support older adults in managing medical treatments and daily schedules. Methods An assistive system was developed combining a social robot and a virtual assistant. Its potential was assessed through an exploratory evaluation involving seven older adults who interacted with the solution in simulated care and schedule management scenarios. Data were collected through structured interviews to capture participants' perceptions and experiences. Results The developed solution supported effective interaction between users and the technologies, despite minor usability challenges during initial use. Participants were generally able to complete tasks such as medication reminders, appointment management, and basic conversational interactions, although some required occasional assistance or clarification. Evaluation The participants expressed positive feedback regarding usability and perceived usefulness. The combined use of social robots and virtual assistants was considered intuitive and supportive, especially in reducing cognitive load and fostering adherence to treatment routines. Conclusion The integrated assistive solution presents a promising approach to supporting older adults' independence and well‐being. By combining social presence with functional assistance, it contributes to bridging the gap between human‐centered care and technological innovation.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".