Modeling the assessment of the upper limb motor function impairment in children with cerebral palsy using sEMG and IMU sensors
Bibliographic record
Abstract
Abstract Several rehabilitation approaches have shown that robot-assisted therapy (robot-AT) can improve the quality of upper limb movements in children with cerebral palsy (CP). However, there is still no method for assessing upper limb motor function impairment using a combination of surface electromyography (sEMG) and inertial measurement unit (IMU) sensors. The aim of this study was to develop a functional ability model to assess the effectiveness of robot-AT on improving upper limb function in children with CP. Fifteen healthy children and fifteen children with CP were included in this study. Children with CP performed eighteen robot-AT sessions and were evaluated twice, using EMG and three-axis IMU readings from accelerometer (IMU-ACC). Principal component analysis and the RELIEFF algorithm were used for dimensionality reduction of the feature space. The classification was performed by using support vector machines, linear discriminant analysis, and random forest. The proposed assessment method was evaluated by using leave-one-out cross validation. With this approach, it was possible to differentiate between healthy children and children with CP pre-robot-AT and post-robot-AT with an overall accuracy of 97.56%. This study suggests that there is potential for modeling the assessment of the upper limb motor function impairment in children with CP using sEMG and IMU-ACC sensors.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".