An adaptive model to support biofeedback in AmI environments: a case study in breathing training for autism

Biofeedback systems have shown promising clinical results in regulating the autonomic nervous system (ANS) of individuals. However, they typically offer a “one-size-fits-all” solution which personalization stimuli to needs and capabilities its users has been largely neglected. Personalization is paramount vulnerable populations like children with autism given their sensory diversity. Ambient intelligence (AmI) environments enable creating effective adaptive mechanisms biofeedback adjust each user’s performance. Yet, models are scarce AmI literature. In this paper, we propose an model support that takes physiological data, adherence therapy, environmental data personalize parameters stimuli. Based on proposed model, present EtherealBreathing, designed help practice box breathing. We used from 20 using EtheralBreating without adaptation feed automatically adapts visual audible EtherealBreathing according changes data. two scenarios showing how capable personalizing stimuli, difficulty level, or supporting therapist decisions. Results terms performance user importance for technology. Finally, discuss challenges opportunities environments.