Augmented utensils for monitoring feeding disorders in children
Feeding disorders can develop in young children from a range of medical, developmental, or psychological causes. As many as one in four infants and children may have feeding disorders. In severe cases, children refuse all food and require a feeding tube. Behavioral psychologists provide one type of treatment, which addresses motivational and skill-based factors that prevent feeding. The treatment views feeding as a skill that must be acquired by young children. Skill acquisition, in this case, occurs through social interactions with the caregiver, who provides motivation and influences the child’s behaviors through their own. Therefore, effective treatment requires the behavioral psychologist to coordinate with parents, school staff, and other caregivers in a child’s life in order to maintain consistent caregiver behaviors that lead to food acceptance.
While this therapy is effective, it is also challenging, time intensive, and requires tight coordination between caregivers and clinicians. In particular, caregivers face significant challenges maintaining proficiency in feeding the child when transitioning to the home setting where clinicians have limited ability to coach parents remotely. Our approach is to create a set of utensils augmented with sensing capabilities that can capture the unique signature of the child’s eating behavior along with behavioral modeling techniques to provide a contextual understanding of successful eating behaviors. The long-term goal is to create a system that can provide real-time feedback to parents on their feeding techniques and provide the ability to capture challenging feeding events so that a remote clinician can examine the situation and offer further, consistent coaching. The goal of the MCubed project will be to construct the augmented utensils with onboard IMUs, camera, microphones and other sensors in a form factor that is usable in a clinical and home setting. This initial research artifact will provide new metrics and insight into eating behaviors and enable future research in this area.
Presented at the Workshop on Interactive Systems in Healthcare (WISH) at SIGCHI Conference on Human Factors in Computing Systems