Development and Implementation of a ML Model to Identify Emotions in Children with SMCI

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.