Personalized Neural Network for Patient-Specific Health Monitoring in IoT: A Metalearning Approach

Abstract

The Internet of Things (IoT) has been widely applied in personal health monitoring on biosignals. Conventional detection methods in the field count on a variety of heuristic criteria by utilizing extracted features, which are carefully selected through extensive clinical trials and experts’ experiences. Recently, deep learning (DL) gains rapidly growing attention in health monitoring. The most significant advantage of DL-based methods is that DL could execute feature engineering automatically with only labeled data, which results in a great reduction in the expertise involved and manual works in the detection method’s design. However, individual differences among various patients (subjects) can lead to accuracy degradation of the pretrained deep model. Simply fine-tuning the deep model with the patient-specific data cannot alleviate the problem since the pretrained model may not generalize well to new data. To address the problem, we propose a metalearning-based personalization method to generate the personalized neural network for each patient to conduct patient-specific detection. Specifically, the proposed metalearning method leverages a novel patientwise training tasks formatting strategy to train the neural network that ends up with a well-generalized model initialization containing across-patient knowledge. The well-generalized model initialization would then be utilized to perform a quick adaptation to the specific patient’s data domain. In this way, a new patient could be immediately assigned with a personalized neural network using limited labeled data. Experimental results show that the proposed metalearning-based personalization method achieves 8.2%, 2.5%, and 6.4% higher accuracy when compared with the existing DL detection methods in VF detection, AF detection, and human activity recognition, respectively.