A Novel Federated Meta-Learning Approach for Discriminating Sedentary Behavior From Wearable Data

Characterizing and monitoring patient activities through time series data is critical for identifying lifestyle patterns that may impact health outcomes. Sedentary behavior is a significant concern due to its association with various health risks. This study introduces a lightweight supervised classifier for healthcare applications based on ordinal pattern (OP) transformation to detect sedentary behavior in federated learning (FL) scenarios. Our hypothesis is grounded on the idea that sedentary behavior exhibits distinct dynamics compared to other activities, and information descriptors derived from the transformation of OPs effectively capture these differences. Next, we proceed with the FL training. We train a neural network (NN)-based encoder locally and send the local models to a server. The FL process updates the encoder weights based on the encoded representations of the clients' data, enabling the model to learn from different participants. Finally, we personalize the model for the specific task of classifying sedentary behavior. Our approach utilizes a meta-learning framework, incorporating a Siamese NN to learn a similarity space. We fine-tune the model in this step by further training the last NN layer. This fine-tuning allows the model to adapt and specialize in accurately classifying sedentary behavior. We carry out a comprehensive analysis to support our hypothesis. We also extensively validated our proposal by comparing it with other methods over five different data sets. We obtain the best results using a smaller machine learning model compared with the best approaches in the literature. Specifically, our model has 78.73% times fewer parameters and consumes 48.67% times less energy than the best result in the literature.