LLMT: A Transformer-Based Multi-Modal Lower Limb Human Motion Prediction Model for Assistive Robotics Applications
Bibliographic record
Abstract
Recognition of human intended motion is key to developing intelligent human-robot interaction (HRI) controllers in assistive devices. This study aims to develop a human motion recognition architecture tailored explicitly for real-time assistive robotics, such as exoskeletons and robot-assisted walking systems. We introduced a multi-modal lower limb modified transformer (LLMT), an architecture that bridges the gap in existing HRI technologies by defining a comprehensive set of relevant motions that generalize well for unseen subjects, ensuring adaptability and precision in diverse interaction scenarios. LLMT uses sparse multi-channel surface electromyography (sEMG) and Inertial Measurement Unit (IMU) signals to classify different motion patterns. The accuracy of the proposed method was compared with that of the classical machine learning (cML) models and a convolutional neural network (CNN). This comparison uses experimental data from seven human participants in two motion scenarios and a benchmark dataset. The validation methods included inter-subject, leave-one-subject-out, and intra-subject approaches. The proposed method demonstrated excellent accuracy, achieving$99.42 \pm 0.25\%$,$99.07 \pm 0.32\%$, and$97.08 \pm 1.16\%$in inter-subject, leave-one-subject-out, and intra-subject validation methods on the collected and benchmark datasets, respectively. Additionally, it exhibited an average online prediction time of 84.09 ms within the recording loop.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".