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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 in which different data distributions, classification classes, and disease detection tasks are introduced sequentially. It counteracts catastrophic forgetting with either a data preservation (DP) method or a synthetic data generation (SDG) module. The DP 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\u2019s efficacy in maintaining high disease classification accuracy with a single DNN model in various CL experiments. In complex scenarios, DOCTOR achieves 1.43\u00d7 better average test accuracy, 1.25\u00d7 better F1-score, and 0.41 higher backward transfer than the na\u00efve fine-tuning framework, with a small model size of less than 350 KB.<\/jats:p>","DOI":"10.1145\/3679050","type":"journal-article","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T11:09:12Z","timestamp":1721992152000},"page":"1-33","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["DOCTOR: A Multi-Disease Detection Continual Learning Framework Based on Wearable Medical Sensors"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9557-6050","authenticated-orcid":false,"given":"Chia-Hao","family":"Li","sequence":"first","affiliation":[{"name":"Electrical and Computer Engineering, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1539-0369","authenticated-orcid":false,"given":"Niraj K.","family":"Jha","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering, Princeton University, Princeton, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"issue":"1","key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.33545\/27076636.2022.v3.i1b.53","article-title":"A machine learning model for skin disease classification using convolution neural network","volume":"3","author":"Allugunti Viswanatha Reddy","year":"2022","unstructured":"Viswanatha Reddy Allugunti. 2022. 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