DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors

Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge devices have enabled ML-driven disease detection for smart healthcare. Conventional ML-driven disease detection methods rely on customizing individual models for each disease and its corresponding WMS data. However, such methods lack adaptability to distribution shifts and new task classification classes. Also, they need to be rearchitected and retrained from scratch for each new disease. Moreover, installing multiple ML models in an edge device consumes excessive memory, drains the battery faster, and complicates the detection process. To address these challenges, we propose DOCTOR, a multi-disease detection continual learning (CL) framework based on WMSs. It employs a multi-headed deep neural network (DNN) and an exemplar-replay-style CL algorithm. The CL algorithm enables the framework to continually learn new missions where different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with a data preservation method and a synthetic data generation module. The data preservation method efficiently preserves the most informative subset of training data from previous missions based on the average training loss of each data instance. The synthetic data generation module models the probability distribution of the real training data and then generates as much synthetic data as needed for replays while maintaining data privacy. The multi-headed DNN enables DOCTOR to detect multiple diseases simultaneously based on user WMS data. We demonstrate DOCTOR's efficacy in maintaining high multi-disease classification accuracy with a single DNN model in various CL experiments. DOCTOR achieves very competitive performance across all CL scenarios relative to the ideal joint-training framework while maintaining a small model size.