Genetic Algorithm-Optimized Feature Selection for sEMG-IMU Fusion Improves Intention Detection in AI-Driven Robotic Walking System
Bibliographic record
Abstract
The increasing demand for responsive and intuitive assistive walking devices, driven by an aging population, underscores the need for intelligent systems powered by emerging machine learning (ML) technologies. This study introduces a novel feature fusion framework based on the Nondominated Sorting Genetic Algorithm II (NSGA-II) to fuse surface electromyography (sEMG) signals with inertial measurement unit (IMU) data and a high-level control architecture, enabling accurate and robust motion intention detection for robotic assistive walking systems. The proposed feature fusion method consistently outperformed statistical filter-based techniques such as mutual information (MI), minimum redundancy maximum relevance (MRMR), correlation coefficient (CC), and Fisher score (FS). It significantly improved the classification metrics of random forest (RF), K-nearest neighbour (KNN), and support vector machine (SVM) classifiers across varying feature counts. For example, the feature fusion algorithm improved RF’s accuracy by 6.74%, 7.67%, 6.35%, and 3.60% and enhanced precision by 6.77%, 7.67%, 6.36%, and 3.61% relative to FS, CC, MRMR, and MI, respectively. Similarly, the algorithm increased RF’s recall by 6.79%, 7.71%, 6.38%, and 3.62%. The proposed feature fusion and high-level and low-level control frameworks were implemented on SoloWalk for real-time interaction, enabling participants to perform daily walking activities. Real-time validation confirmed system stability across gait patterns and user variations, demonstrating its effectiveness in assistive walking robots.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".