Unsupervised Cross-Subject Adaptation for Predicting Human Locomotion Intent
Bibliographic record
Abstract
Accurately predicting human locomotion intent is beneficial in controlling wearable robots and in assisting humans to walk smoothly on different terrains. Traditional methods for predicting human locomotion intent require collecting and labeling the human signals, and training specific classifiers for each new subject, which introduce a heavy burden on both the subject and the researcher. In addressing this issue, the present study liberates the subject and the researcher from labeling a large amount of data, by incorporating an unsupervised cross-subject adaptation method to predict the locomotion intent of a target subject whose signals are not labeled. The adaptation is realized by designing two classifiers to maximize the classification discrepancy and a feature generator to align the hidden features of the source and the target subjects to minimize the classification discrepancy. A neural network is trained by the labeled training set of source subjects and the unlabeled training set of target subjects. Then it is validated and tested on the validation set and the test set of target subjects. Experimental results in the leave-one-subject-out test indicate that the present method can classify the locomotion intent and activities of target subjects at the averaged accuracy of 93.60% and 94.59% on two public datasets. The present method increases the user-independence of the classifiers, but it has been evaluated only on the data of subjects without disabilities. The potential of the present method to predict the locomotion intent of subjects with disabilities and control the wearable robots will be evaluated in future work.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".