The proliferation in embedded and communication technologies made the concept of the Internet of Medical Things (IoMT) a reality. Individuals’ physical and physiological status can be constantly monitored, and numerous data can be collected through wearable and mobile devices. However, the silo of individual data brings limitations to existing machine learning approaches to correctly identify a user’s health status. Distributed machine learning paradigms, such as federated learning, offer a potential solution for privacy-preserving knowledge sharing without sending raw personal data. However, federated learning is vulnerable to harmful participants that can degrade the overall model quality by sharing low-quality data. Therefore, it is critical to select suitable participants to ensure the accuracy and efficiency of federated learning. In this article, a unique clustering-based approach is proposed to use social context data for participant selection. Different edge participant groups will be established, and group-specific federated learning will be performed. The models of various edge groups will be further aggregated to strengthen the robustness of the global model. The experimental results demonstrated that through participant selection, clustering-based hierarchical federated learning can achieve better results with less participants in two different IoMT applications for ECG and human motion monitoring. This shows the efficacy of the proposed method in improving federated learning performance and efficiency in various IoMT applications.

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges.

Sensor-based human activity recognition (HAR) is a cornerstone of ubiquitous computing, playing a crucial role in modern consumer electronics such as fitness trackers and smart home systems. Despite significant advancements, traditional sensor-based HAR methods often assume that training and testing data share identical distributions, an assumption invalidated in real-world scenarios by out-of-distribution (o.o.d.) challenges, including heterogeneous sensors, temporal changes, and individual behavioral variability. This paper addresses the cross-user sensor-based HAR problem, where individual behavioral differences lead to varying data distributions. We introduce the Deep Temporal State Domain Adaptation (DTSDA) model, an innovative approach tailored for time series domain adaptation in cross-user sensor-based HAR. Unlike existing domain adaptation methods that assume sample independence, DTSDA leverages temporal dependencies inherent in sensor data. We introduce the concept of ‘Temporal State’ to define sub-activities within an activity and ensure their temporal sequence through the ‘Temporal Consistency’ property. The ‘Pseudo Temporal State Labeling’ method identifies user-invariant temporal dependency relations. Integrating adversarial learning with temporal dependency knowledge, our method enhances classification performance in cross-user HAR. The efficacy of DTSDA is demonstrated through the evaluations on three public datasets, focusing on daily living and sports fitness activities using consumer electronics.