iEDA-LSTM: Intelligent EDA-Guided Deep Learning for Real-Time Substance Use Detection in Biosensor Data Streams
Dissertation advisor: Dr. (Julia) Hua Fang
Committee members:
- Dr. Jiawei Yuan
- Dr. Honggang Wang
- Dr. Gang Zhou
Abstract:
Electrodermal Activity (EDA), an indicator of sympathetic nervous system activation, has been widely examined in psychophysiological research but remains relatively underexplored in the context of substance use detection. This dissertation presents iEDA-LSTM, a real-time substance use detection framework that integrates EDA-based biophysiological sensing, longitudinal signal modeling, and deep learning. The study systematically characterizes EDA signal dynamics associated with cocaine use and implements an end-to-end detection pipeline encompassing biosensor data acquisition, preprocessing, temporal feature extraction, and real-time event prediction using a Long Short-Term Memory (LSTM) network adapted for substance use data streams. Multiple labeling strategies are developed to define onset and event windows of cocaine use, with various smoothing techniques, Savitzky–Golay, Gaussian, and rolling-average filters, applied to construct stable event-window outcomes. To identify predictive relationships between candidate outcomes and multimodal predictors derived from EDA and temperature data, feature alignment and temporal similarity mapping are employed to optimize input–output pairing for model training. The iEDA-LSTM model demonstrates consistent predictive performance, with an average F1-score of 0.8 and mean squared error (MSE) of 0.039 across five-fold cross-validation. Comparative evaluations with conventional machine learning models indicate an approximate 25% performance improvement in overall predictive metrics. Additional analyses examine model extensions incorporating attention mechanisms and L1 regularization to evaluate their effects on interpretability and generalization. Beyond algorithmic evaluation, this dissertation demonstrates a live emulated real-time deployment of iEDA-LSTM. Using a Samsung Galaxy Tab S7+ as a biosensor data interface and a Windows workstation with an NVIDIA RTX A4500 GPU for inference, the system performs continuous per-window prediction to identify potential cocaine use events from both historical real-time data streams. This research contributes to both methodological and applied domains. Methodologically, it advances temporal deep learning approaches for physiological signal interpretation and introduces a validated real-time framework for event-based biosensing. From an application perspective, it supports the development of real-time substance use monitoring systems that may facilitate earlier detection and intervention. The iEDA-LSTM framework offers a scalable basis for future adaptive, sensor-driven technologies in mental and behavioral health monitoring.
All EAS students are encouraged to attend and all interested parties invited. For further information, please contact Dr. Hua Fang.
Zoom Meeting
Dr. Hua (Julia) Fang
jfang2@umassd.edu
https://umassd.zoom.us/j/95683754446?pwd=WW1paGw5Q29mdXBMb0E3N3dkUTZ2Zz09