Assistive Robots for Patients With Amyotrophic Lateral Sclerosis: Exploratory Task-Based Evaluation Study With an Early-Stage Demonstrator
Bibliographic record
Abstract
BACKGROUND: Although robotic manipulators have great potential in promoting motor independence of people with motor impairments, only few systems are currently commercially available. In addition to technical, economic, and normative barriers, a key challenge for their distribution is the current lack of evidence regarding their usefulness, acceptance, and user-specific requirements. OBJECTIVE: Against this background, a semiautonomous robot system was developed in the research and development project, robot-assisted services for individual and resource-oriented intensive and palliative care of people with amyotrophic lateral sclerosis (ROBINA), to support people with amyotrophic lateral sclerosis (ALS) in various everyday activities. METHODS: The developed early-stage demonstrator was evaluated in a task-based laboratory study of 11 patients with ALS. On the basis of a multimethod design consisting of standardized questionnaires, open-ended questions, and observation protocols, participants were asked about its relevance to everyday life, usability, and design requirements. RESULTS: Most participants considered the system to provide relevant support within the test scenarios and for their everyday life. On the basis of the System Usability Scale, the overall usability of the robot-assisted services for individual and resource-oriented intensive and palliative care of people with ALS system was rated as excellent, with a median of 90 (IQR 75-95) points. Moreover, 3 central areas of requirements for the development of semiautonomous robotic manipulators were identified and discussed: requirements for semiautonomous human-robot collaboration, requirements for user interfaces, and requirements for the adaptation of robotic capabilities regarding everyday life. CONCLUSIONS: Robotic manipulators can contribute to increase the autonomy of people with ALS. A key issue for future studies is how the existing ability level and the required robotic capabilities can be balanced to ensure both high user satisfaction and effective and efficient task performance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".