Predicting Anxiety and Anger Incidents with Sensor-embedded Wearables
Student: Matthew Fritzie, 2015-2016
Sponsor: Prof. Jim Schmiedler, Aerospace and Mechanical Engineering, Notre Dame, IN
Many persons living with cognitive disabilities have anger and anxiety episodes which cause immense frustration for both the individual and caregiver amplified by his/her communicative challenges and reduced self-awareness. This project seeks to utilize data from a combination of sensors available on publicly accessible health-monitoring devices such as watches to determine when an individual is beginning to show signs of anxiety and/or anger that could result in a more serious incident of severe verbal, physical and emotional outburst quantified by physiological hyper arousal. Through data mining a predictive algorithm will be developed and then used to alert the individual and/or caregiver that there is a need to adjust his/her environment/stimuli or take steps in order to restore his/her serenity before the incident becomes inevitable.