RobustPDx: A Structured Web-Based Parkinson’s Assessment Dataset and Benchmark for AI Robustness to Device Type and Handedness ↗
Z. N. Tumpa, M. R. S. Zawad, L. Sollis, S. Parab, P. Azizian, M. Honarmand, A. Kargarandehkordi, et al.
Selected research across digital health, wearable sensing, and personalized AI.
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2026
Personalized modeling of stress and blood pressure reactivity using mobile health data ↗
A. Kargarandehkordi, A. Jaiswal, A. Banerjee, Y. Qian, C. R. Slade, Y. Sun, et al.
Person-Specific Blood Pressure Spike Modeling Using Wearable Data Across Multiple Temporal Aggregation Scales
A. Kargarandehkordi, A. Banerjee, A. Jaiswal, Y. Qian, C. R. Slade, Y. Sun, et al.
Correlates of Fitness Tracker Ownership and Use in Cancer Survivors: Cross-Sectional Survey
R. M. Benzo, J. L. Fisher, M. K. Tetrick, et al., A. Kargarandehkordi, et al.
2025
Fusing wearable biosensors with artificial intelligence for mental health monitoring: A systematic review ↗
A. Kargarandehkordi, S. Li, K. Lin, K. T. Phillips, R. M. Benzo, P. Washington
Hashtag2Action: Data Engineering and Self-Supervised Pre-Training for Action Recognition in Short-Form Videos ↗
Y. Qian, A. Kargarandehkordi, Y. Sun, P. Azizian, O. C. Mutlu, S. Surabhi, et al.
2024
Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing ↗
S. Li, C. Fan, A. Kargarandehkordi, Y. Sun, C. Slade, A. Jaiswal, et al.
Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study ↗
A. Kargarandehkordi, M. Kaisti, P. Washington
Personalized AI-Driven Real-Time Models to Predict Stress-Induced Blood Pressure Spikes Using Wearable Devices: Protocol
A. Kargarandehkordi, et al.
Personalized Deep Learning for Substance Use in Hawaii: Protocol for a Passive Sensing and Ecological Momentary Assessment Study
Y. Sun, A. Kargarandehkordi, C. Slade, A. Jaiswal, et al.
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