Computer scientists at Microsoft Research and the University of California, San Diego have developed the system that delivers research-based strategies to help decrease stress in parents during emotionally charged interactions with their children.
The system, called ParentGuardian, was initially tested on a small group of parents of children with Attention Deficit Hyperactivity Disorder (ADHD).
The system is the first to detect stress and present interventions in real-time.
It combines a sensor worn on the wrist with a smart phone and tablet, and a server to analyse data from the sensor.
The therapy teaches parents the skills they need to work on and has been shown to have long-term effects for both parent and child.
It has been shown to improve self-control and self-awareness in children and reduce parental stress.
But sticking with the therapy is difficult, especially during times of the day that are particularly stressful.
ParentGuardian was designed to identify these stressful moments and remind parents of these strategies, which they sometimes forget in the heat of the moment.
Overall, parents reported that the app was very helpful, giving it an average rating of 5.1 on a scale of 1 to 7.
ParentGuardian combines four different pieces. The first is the stress sensor, the second is the phone, which reminds parents of effective strategies and also transmits data from the sensor to a backend server, where the sensor data is analysed to detect when the parent is stressed.
The wrist sensor measures electrical activity on the user's skin. When users experience positive or negative feelings, they secrete very small quantities of sweat, which changes the amount of electricity their skin conducts.
The sweat is not visible to the naked eye, but is enough to change the amount of electricity conducted by the skin, which is used for stress detection.
Users also self-reported when they were feeling stress throughout the day as a form ground truth.
Researchers then compared the data from the sensors with the users' self-reports about stress to train a machine learning algorithm to detect the stress events in real-time.
