Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
This work addresses mental health monitoring for adults by enabling more accurate predictions for targeted interventions, but it is incremental as it builds on existing passive sensing methods.
The paper tackled the problem of predicting negative affect states from passively sensed data by personalizing recognition through group-based modeling of user behavior patterns, resulting in group models outperforming generalized models on a two-week dataset.
Negative affect is a proxy for mental health in adults. By being able to predict participants' negative affect states unobtrusively, researchers and clinicians will be better positioned to deliver targeted, just-in-time mental health interventions via mobile applications. This work attempts to personalize the passive recognition of negative affect states via group-based modeling of user behavior patterns captured from mobility, communication, and activity patterns. Results show that group models outperform generalized models in a dataset based on two weeks of users' daily lives.