User Acceptance of a Home Robotic Assistant for Individuals With Physical Disabilities: Explorative Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Health care is shifting toward 5 proactive approaches: personalized, participatory, preventive, predictive, and precision-focused services (P5 medicine). This patient-centered care leverages technologies such as artificial intelligence (AI)-powered robots, which can personalize and enhance services for users with disabilities. These advancements are crucial given the World Health Organization's projection of a global shortage of up to 10 million health care workers by 2030. OBJECTIVE: This study aimed to investigate the acceptance of a humanoid assistive robot among users with physical disabilities during (1) AI-powered (using a Wizard of Oz methodology) robotic performance of predefined personalized assistance tasks and (2) operator-controlled robotic performance (simulated distant service). METHODS: An explorative qualitative design was used, involving user testing in a simulated home environment and individual interviews. Directed content analysis was based on the Almere model and the model of domestic social robot acceptance. RESULTS: Nine participants with physical disabilities aged 27 to 78 years engaged in robot interactions. They shared their perceptions across 7 acceptance concepts: hedonic attitudes, utilitarian attitudes, personal norms, social norms, control beliefs, facilitating conditions, and intention to use. Participants valued the robot's usefulness for practical services but not for personal care. They preferred automation but accepted remote control of the robot for some tasks. Privacy concerns were mixed. CONCLUSIONS: This study highlights the complex interplay of functional expectations, technological readiness, and personal and societal norms affecting the acceptance of physically assistive robots. Participants were generally positive about robotic assistance as it increases independence and lessens the need for human caregivers, although they acknowledged some current shortcomings. They were open to trying more home testing if future robots could perform most tasks autonomously. AI-powered robots offer new possibilities for creating more adaptable and personalized assistive technologies, potentially enhancing their effectiveness and viability for individuals with disabilities.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".