🤖 AI Summary
To address the challenge of early identification of depressive comorbidities in chronic disease patients, this paper proposes TempPNet—a temporally aware, prototype-based interpretable deep neural network that enables real-time depression risk monitoring and dynamic progression modeling using wearable IoT motion sensor data. TempPNet is the first to integrate prototype learning into temporal representation learning for depression, facilitating clinically interpretable symptom attribution and human-AI collaborative decision-making. Evaluated on real-world sensor data, it significantly outperforms state-of-the-art models. User studies and expert clinical evaluations confirm its high explanation quality and clinical utility. The lightweight architecture supports efficient mobile deployment. Our core contributions are: (1) the first temporal prototype framework explicitly designed for modeling depression’s dynamic evolution; and (2) a principled integration of predictive performance, algorithmic interpretability, and clinical trustworthiness.
📝 Abstract
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 detection using motion sensor data. To connect humans in this decision-making, safeguard trust, 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 temporal progressions of prototypes. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression detection. Moreover, TempPNet interprets its decision by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further employ a user study and a medical expert panel 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 detection 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 and making informed interventions.
