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Objective Measurement of Hyperactivity Using Mobile Sensing and Machine Learning: Pilot Study
Oliver Lindhiem1, Mayank Goel2, Sam Shaaban3
1Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States.
Background:
Although hyperactivity is a core symptom of attention-deficit/hyperactivity disorder (ADHD), there are no objective measures that are widely used in clinical settings.
Objective:
We describe the development of a smartwatch app to measure hyperactivity in school-age children. The LemurDx prototype is a software system for smartwatches that uses wearable sensor technology and machine learning to measure hyperactivity. The goal is to differentiate children with ADHD combined presentation (a combination of inattentive and hyperactive/impulsive presentations) or predominantly hyperactive/impulsive presentation from children with typical levels of activity.
Methods:
In this pilot study, we recruited 30 children, aged 6 to 11 years, to wear a smartwatch with the LemurDx app for 2 days. Parents also provided activity labels for 30-minute intervals to help train the algorithm. Half of the participants had ADHD combined presentation or predominantly hyperactive/impulsive presentation (n=15), and half were in the healthy control group (n=15).
Results:
The results indicated high usability scores and an overall diagnostic accuracy of 0.89 (sensitivity=0.93; specificity=0.86) when the motion sensor output was paired with the activity labels.
Conclusions:
State-of-the-art sensors and machine learning may provide a promising avenue for the objective measurement of hyperactivity.
