  Health sensing for chronic disease management creates immense benefits for
social welfare. Existing health sensing studies primarily focus on the
prediction of physical chronic diseases. Depression, a widespread complication
of chronic diseases, is however understudied. We draw on the medical literature
to support depression prediction using motion sensor data. To connect human
expertise in the decision-making, safeguard trust for this high-stake
prediction, and ensure algorithm transparency, we develop an interpretable deep
learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon
the emergent prototype learning models. To accommodate the temporal
characteristic of sensor data and the progressive property of depression,
TempPNet differs from existing prototype learning models in its capability of
capturing the temporal progression of depression. Extensive empirical analyses
using real-world motion sensor data show that TempPNet outperforms
state-of-the-art benchmarks in depression prediction. Moreover, TempPNet
interprets its predictions by visualizing the temporal progression of
depression and its corresponding symptoms detected from sensor data. We further
conduct a user study to demonstrate its superiority over the benchmarks in
interpretability. This study offers an algorithmic solution for impactful
social good - collaborative care of chronic diseases and depression in health
sensing. Methodologically, it contributes to extant literature with a novel
interpretable deep learning model for depression prediction from sensor data.
Patients, doctors, and caregivers can deploy our model on mobile devices to
monitor patients' depression risks in real-time. Our model's interpretability
also allows human experts to participate in the decision-making by reviewing
the interpretation of prediction outcomes and making informed interventions.
