Activity Recognition Using Constrained IoT Devices

Dear Colleagues,

Understanding human behavior in an automatic and non-intrusive manner constitutes an important and emerging area of research within pervasive systems. With the rapid development of the Internet of Things (IoT), combined with advances in machine/deep learning, technology-based solutions to automatically detect and model human behaviors are becoming possible. This technology can support services such as activity recognition, fall detection, behavior modelling and risk determination. Recently, there has been a move toward edge computing as a way to reduce communication latency and network communication whilst preserving privacy. Various solutions have been developed to support modelling of human behavior. In particular, deep learning algorithms have shown high performance for applications such as human activity recognition. These algorithms, however, typically require large amounts of computation for training and inference, making them unsuitable for deployment on resource-constrained edge devices. Devices in a resource-constrained environment become even more challenging when they are battery-powered, such as is the case with wearable applications, making them computationally intensive and power demanding.

This Special Issue seeks to bring together innovative research solutions in the area of behavioral modeling specifically for constrained devices. Authors are invited to submit original articles across the full development stack (hardware, system, software and applications), including architectures, techniques, tools and approaches on the device modelling of human behaviors. This may include, but is not limited to, new and novel sensing modalities (audio, vision, environment, health), strategies for data collection, annotation and labelling, personalization, sensor fusion, computational constraints reduction, extreme energy efficiency, model optimization, and federated learning, as well as examining the performance of these solutions in real-world settings with diverse populations.

Dr. Ian Cleland
Prof. Dr. Oresti Banos
Guest Editors

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