Development and Implementation of a ML Model to Identify Emotions in Children with SMCI
Bibliographic record
Abstract
Children with severe motor and communication impairments (SMCI) face significant challenges in expressing emotions, often leading to unmet needs and social isolation. This study investigates the potential of machine learning to identify emotions in children with SMCI through the analysis of physiological signals. A model was created based on the data from the DEAP online dataset to identify emotions of typically developing (TD) participants. The DEAP model was then adapted for use by participants with SMCI using data collected within the Building and Designing Assistive Technology Lab (BDAT). Key adaptations of the DEAP model resulted in the exclusion of respiratory signals, reduction of wavelet levels, and analysis of shorter-duration data segments to enhance model applicability. The adapted SMCI model demonstrated accuracy comparable to the DEAP model, performing better than chance in TD populations and showing promise for adaptation to SMCI contexts. The models were not reliable for effective identification of emotion, however these findings highlight the feasibility of using machine learning to bridge communication gaps for children with SMCI, enabling better emotional understanding. Future efforts should focus on expanding data collection of physiological signals for diverse populations and developing personalized models to account for individual differences. This study underscores the importance of collecting data from populations of SMCI for development of inclusive technologies in promoting empathetic care and enhancing the quality of life for non-communicative children.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".