Assistive Robotic Arm to Support Activities of Daily Living in Individuals with Tetraplegia: Protocol for a Real-World Convergent Parallel Mixed Methods Feasibility Study
Bibliographic record
Abstract
Tetraplegia is a severe form of spinal cord injury (SCI) resulting from damage to the cervical spine, leading to partial or complete loss of motor and/or sensory function in all four extremities and the trunk (1,2). Globally, SCI affects approximately 15.4 million persons (3), with an annual incidence of 250,000 to 500,000 new cases according to the World Health Organization (2). Individuals with tetraplegia face extensive challenges in daily life, including limitations in mobility, personal care, and social participation (2,4). These restrictions significantly reduce autonomy and quality of life (QoL), while also placing a substantial economic burden on both affected individuals and healthcare systems. According to Pacheco Barzallo et al. (2024), it has been estimated that, persons with SCI in Switzerland use healthcare services 11 times more (including physiotherapists, nursing services, general practitioners and specialists) than the healthy population and 4 times more than persons with other chronic health conditions (5). Moreover, caregivers, especially family members providing unpaid care, often experience heightened psychological stress and are at increased risk of developing mental health conditions (4,6). Assistive technologies (ATs) play a crucial role in mitigating the effects of physical impairments by enhancing independence and enabling participation in activities of daily living (ADLs) (3,7,8). AT encompasses a wide range of tools, from adaptive cutlery to advanced robotic systems. Individuals with tetraplegia particularly benefit from wheelchair-mounted robotic arms (WMRAs), which enhance care and promote independence across different areas (9,10). Examples include the Functional Robot with Dexterous Arm and User Friendly Interface for Disabled People (FRIEND) system, a wheelchair-mounted robotic manipulator designed to assist users with tetraplegia in tasks such as drinking and eating (11) or the Jaco 2 robotic arm (Kinova Inc., Boisbriand, QC, Canada), which has been applied in various contexts, such as adaptive feeding systems (12). However, their adoption into daily life remains limited. Most devices are still in research or prototype phases, tested primarily in controlled experimental settings with able-bodied participants (7). The high costs, lack of personalization, and the need for end-users to be heavily involved in the development process contribute to the low acceptance and small market transfer of these devices (3,4,13). The three robotic arm models which have reached the commercial market (the Exxomove Bateo, the iARM and the JACO robotic arm) all lack robust scientific evidence demonstrating their long-term efficacy for individuals with tetraplegia (7,14,15), furthermore, most published research focuses on technical feasibility only (4). The current state of research, along with the lack of high-quality studies evaluating practical effectiveness of assistive robotic systems for individuals with tetraplegia in post-hospitalization settings, led us to the following objectives of our study: Evaluate the feasibility of a WMRA in supporting ADLs for individuals with tetraplegia. Assess user satisfaction, usability, and perceived autonomy in ADLs involving the robotic arm. Collect qualitative and quantitative data using a mixed methods approach to inform further development of user-centered robotic AT systems. Conduct a health economic analysis to assess the cost-effectiveness of the robotic arm in everyday use compared to formal / informal care, considering both direct and indirect costs (e.g. care time, productivity loss), and linking these to outcomes (health-related QoL, perceived independence).
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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.014 |
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".