  Mobile digital therapeutics for autism spectrum disorder (ASD) often target
emotion recognition and evocation, which is a challenge for children with ASD.
While such mobile applications often use computer vision machine learning (ML)
models to guide the adaptive nature of the digital intervention, a single model
is usually deployed and applied to all children. Here, we explore the potential
of model personalization, or training a single emotion recognition model per
person, to improve the performance of these underlying emotion recognition
models used to guide digital health therapies for children with ASD. We
conducted experiments on the Emognition dataset, a video dataset of human
subjects evoking a series of emotions. For a subset of 10 individuals in the
dataset with a sufficient representation of at least two ground truth emotion
labels, we trained a personalized version of three classical ML models on a set
of 51 features extracted from each video frame. We measured the importance of
each facial feature for all personalized models and observed differing ranked
lists of top features across subjects, motivating the need for model
personalization. We then compared the personalized models against a generalized
model trained using data from all 10 participants. The mean F1-scores achieved
by the personalized models were 90.48%, 92.66%, and 86.40%, respectively. By
contrast, the mean F1-scores reached by non-personalized models trained on
different human subjects and evaluated using the same test set were 88.55%,
91.78%, and 80.42%, respectively. The personalized models outperformed the
generalized models for 7 out of 10 participants. PCA analyses on the remaining
3 participants revealed relatively facial configuration differences between
emotion labels within each subject, suggesting that personalized ML will fail
when the variation among data points within a subjects data is too low.
