FEELS: a full-spectrum enhanced emotion learning system for assisting individuals with autism spectrum disorder
Bibliographic record
Abstract
Autism Spectrum Disorder (ASD) is a developmental disorder thatcan lead to a variety of social and communication challenges, andindividuals with ASD are at a higher risk of loneliness and depres-sion as a result of the disconnect and isolation they may feel fromthe rest of society as a result of their ASD. Interventions targetingimproved emotional detection has been clinically shown to be quitepromising; however, there are considerable barriers that make itchallenging to incorporate emotion detection within daily life sce-narios. Motivated by the need to fill this gap, we introduce theconcept of FEELS, a full-spectrum enhanced emotion learning sys-tem which could be useful as a tool to assist individuals with ASD.FEELS facilitates enhanced emotion detection by capturing a livevideo stream of individuals in real-time, then leveraging deep con-volutional neural networks to detect facial landmarks and a customhybrid neural network consisting of a time distributed feed-forwardneural network and a LTSM neural network to determine the emo-tional state of the individuals based on a sequence of facial land-marks over time. The feasibility of such an approach was exploredthrough the construction of a proof-of-concept FEELS system thatcan detect between five different basic emotional states: neutral,sad, happy, surprise, and anger. Future work will include extend-ing the proof-of-concept FEELS system to detect more emotionalstates and evaluate the system in more natural settings.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".