Man-machine interaction-based motion control of a robotic walker
Bibliographic record
Abstract
BACKGROUND: The aging population brings the problem of healthcare and dyskinesia. The lack of mobility extremely affects stroke patient's activities of daily living (ADL) and decreases their quality of life. To assist these mobility-limited people, a robotic walker is designed to facilitate gait rehabilitation training. OBJECTIVE: The aim of this paper is to present the implementation of a novel motion control method to assist disabled people based on their motion intention. METHODS: The kinematic framework of the robotic walker is outlined. We propose an intention recognition algorithm based on the interactive force signal. A novel motion control method combined with T-S fuzzy controller and PD controller is proposed. The motion controller can recognize the intention of the user through the interactive force, which allows the user to move or turn around as usual, instead of using their hands to control the walker. RESULTS: Preliminary experiments with healthy individuals and simulated patients are carried out to verify the effectiveness of the algorithm. The results show that the proposed motion control approach can recognize the user's intention, is easy to control and has a higher precision than the traditional proportional-integral-derivative controller. CONCLUSION: The results show that users could achieve the task with acceptable error, which indicates the potential of the proposed control method for gait training.
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".