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Recently, the importance of weather parameters and location information tobetter understand the context of the communication of children with profoundintellectual and multiple disabilities (PIMD) or severe motor and intellectualdisorders (SMID) has been proposed. However, an investigation on whether thesedata can be used to classify their behavior for system optimization aimed forpredicting their behavior for independent communication and mobility has notbeen done. Thus, this study investigates whether recalibrating the datasetsincluding either minor or major behavior categories or both, combining locationand weather data and feature selection method training (Boruta) would allowmore accurate classification of behavior discriminated to binary and multiclassclassification outcomes using eXtreme Gradient Boosting (XGB), support vectormachine (SVM), random forest (RF), and neural network (NN) classifiers.Multiple single-subject face-to-face and video-recorded sessions were conductedamong 20 purposively sampled 8 to 10 -year old children diagnosed withPIMD/SMID or severe or profound intellectual disabilities and their caregivers.

Machine-learning-based investigation on classifying binary and multiclass behavior outcomes of children with PIMD/SMID