An Automated Machine Learning Approach Applied to Robotic Stroke Rehabilitation
Bibliographic record
Abstract
While machine learning methods have proven to be a highly valuable tool in solving numerous problems in assistive technology, state-of-the-art machine learning algorithms and corresponding results are not always accessible to assistive technology researchers due to required domain knowledge and complicated model parameters. This short paper highlights the use of recent work in machine learning to entirely automate the machine learning pipeline, from feature extraction to classification. A nonparametrically guided autoencoder is used to extract features and perform classification while Bayesian optimization is used to automatically tune the parameters of the model for best performance. Empirical analysis is performed on a real-world rehabilitation research problem. The entirely automated approach significantly outperforms previously published results using carefully tuned machine learning algorithms on the same data. As better healthcare worldwide is improving longevity and the baby boomer generation is aging, the proportion of elderly adults within the population is rapidly growing. Healthcare systems and governments are seeking new ways to alleviate the burden on society of caring for this aging population. Artificial intelligence has been shown to be a promising solution, as many of the simpler tasks that burden caregivers can be automated. This also suggests solutions for promoting independence and aging in place, because it alleviates the need for the constant presence of a caregiver in the home. The benefits of the application of machine learning to problems in assistive technology are becoming ever more clear. However, the application of machine learning to problems in assistive technology remains challenging. In particular, it is often unclear what machine learning model or approach is most appropriate for a given task. A common paradigm is to apply multiple standard machine learning tools in a black box manner and compare the results. This proceeds according to the following steps: 1. Collect data representative of the problem of interest. 2. Extract a set of features from these data. Copyright c 2012, Association for the Advancement of Artificial
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".