Design and analysis of a novel fall prevention device for lower limbs rehabilitation robot
Bibliographic record
Abstract
BACKGROUND: Most stroke survivors are suffering from physical motor impairments and confronting with the risk of falls, and well trunk stability is essential for balance during daily functional activities. OBJECTIVES: Current fall prevention devices have various limits to the efficient recovery of balance function of the trunk. To provide hemiplegic patients after stroke with the retraining of trunk position sense and a safety environment, a novel fall prevention device is proposed. METHODS: Firstly, the structure of the device is introduced and this work is a first effort towards restoring trunk balance function through retraining of trunk position sense. Secondly, the kinematic and static model of the device are developed. Lastly, kinematic and static analysis are carried out to study the motion characteristics, and a contrast experiment was derived to show the effectiveness of robot. RESULTS: No obvious difference in balance ability between two groups prior treatment (P> 0.05). Fugl-Meyer assessment in all the cases were improved in different extent (P< 0.05). The robot group had significantly higher Fugl-Meyer scores after treatment than the control group (P< 0.05). CONCLUSIONS: The results show that the fall prevention device has good kinematic dexterity within the prescribed workspace and markedly improves balance function.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".