4 Hz, 4 Pages: Just-in-Time Substance Use Relapse Risk Detection from Wearable Time Series Data

Abhishek Singh Dhadwal

Keywords: Wearable time series, Stress and Affect Detection, Relapse Risk Prediction, Just-in-time adaptive interventions, JITAI, TS4H, Digital Health, mHealth, Multimodal physiological signals, Lightweight time series models, CPU-Efficient Models

TL;DR: We present a lightweight, CPU-efficient pipeline for streaming relapse risk detection from wearable stress and affect datasets, with benchmarks on WESAD, PhysioNet Stress, and CAN-Stress, supporting online inference in JITAI deployment.

Abstract: Substance use relapse is strongly associated with heightened stress and affective states, underscoring the importance of early detection and intervention. Wearable sensing provides a scalable pathway for delivering just-in-time adaptive interventions (JITAIs), but deployment in real-world, resource-constrained environments demands models that are lightweight, deterministic, and CPU-efficient. We introduce a streamlined pipeline that resamples multimodal wearable signals to 4 Hz, applies sliding-window segmentation, and supports two model families: MiniRocket with ridge regression for deterministic accuracy, and a compact statistical feature baseline with logistic regression for online adaptability. Using three publicly available stress/affect datasets (WESAD, PhysioNet Stress, and CAN-Stress) as proxies for relapse risk, we evaluate performance across standard and early-warning metrics, including AUPRC, AUROC, F1 at the optimal threshold, time-to-detection (TTD) at 80% recall, and per-window CPU latency. Results demonstrate competitive predictive performance with latencies consistently under 2 ms per 30 s window, highlighting the feasibility of real-time, streaming inference on commodity hardware. By emphasizing transparent, reproducible evaluation and proxy-to-relapse framing, our work provides a robust foundation for future clinical validation and has potential to enable equitable, low-resource, and globally scalable digital health interventions.

Submission Number: 83