Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
Motion sensing is a cutting-edge area in chronic disease management. Depression, a widespread complication of chronic diseases, is neglected in those studies. We draw on medical literature to endorse depression prediction using motion sensor signals. To safeguard trust for this high-stake decision, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). Because of the temporal feature of sensor signals and the progressive property of depression, TempPNet innovatively modifies existing prototype learning models by capturing the temporal symptom progression of depression. Our empirical results indicate TempPNet outperforms state-of-the-art models in predicting depression. We also interpret our prediction via the visualization of the depression temporal progression and its corresponding symptoms detected in the walking sensor signals. We contribute to the data science methodology with a temporal symptom progression-based prototype network. Patients, doctors, and caregivers can utilize our model on mobile devices to access patients’ depression risks in real-time.
Recommended Citation
Xie, Jiaheng; Zhao, Xiaohang; Liu, Xiang; and Fang, Xiao, "Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/in/impacts/3
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT
Hilton Waikoloa Village, Hawaii
Motion sensing is a cutting-edge area in chronic disease management. Depression, a widespread complication of chronic diseases, is neglected in those studies. We draw on medical literature to endorse depression prediction using motion sensor signals. To safeguard trust for this high-stake decision, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). Because of the temporal feature of sensor signals and the progressive property of depression, TempPNet innovatively modifies existing prototype learning models by capturing the temporal symptom progression of depression. Our empirical results indicate TempPNet outperforms state-of-the-art models in predicting depression. We also interpret our prediction via the visualization of the depression temporal progression and its corresponding symptoms detected in the walking sensor signals. We contribute to the data science methodology with a temporal symptom progression-based prototype network. Patients, doctors, and caregivers can utilize our model on mobile devices to access patients’ depression risks in real-time.
https://aisel.aisnet.org/hicss-58/in/impacts/3