Safety in Wearable Robotic Exoskeletons: Design, Control, and Testing Guidelines
Bibliographic record
Abstract
Abstract Exoskeletons, wearable robotic devices designed to enhance human strength and endurance, find applications in various fields such as healthcare and industry; however, stringent safety measures should be adopted in such settings. This paper presents a comprehensive exploration of challenges associated with exoskeleton technology, ranging from mechanical issues to regulatory and ethical considerations. The enumerated challenges include joint hyper-extension or flexion, rapid or sudden motion, misalignment, fit, and comfort issues, mechanical failure, weight and mobility limitations, environmental challenges, power supply issues, high energy consumption and regeneration, fall risk or stability concerns, sensor failures, control algorithm malfunctions, machine-learning model challenges, communication disconnection, actuator malfunctions, unexpected human–robot interactions, and regulatory and ethical considerations. The paper outlines possible risks and suggests practical solutions based on design, control, and testing methods for each challenge. The objective is to offer a guideline for developers and users, emphasizing safety, reliability, and optimal performance in the ever-evolving landscape of exoskeleton technology. The guideline covers preoperation checks, user training, emergency response, real-time monitoring, and user interaction to ensure responsible innovation and user-centricity in exoskeleton development and deployment.
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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".