  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 methods for disease detection 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. In addition, 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 a 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 (SDG) module. The data preservation method preserves the most
informative subset of real training data from previous missions for exemplar
replay. The SDG module models the probability distribution of the real training
data and generates synthetic data for generative replay while retaining 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 disease classification accuracy with a single DNN model in
various CL experiments. In complex scenarios, DOCTOR achieves 1.43 times better
average test accuracy, 1.25 times better F1-score, and 0.41 higher backward
transfer than the naive fine-tuning framework with a small model size of less
than 350KB.

Machine Learning;Human-Computer Interaction;Signal Processing