Sensor-Based Human Activity Recognition in Real-World Scenarios

Dear Colleagues,

As we have witnessed over the last few decades, more and more smart homes, wearable-based systems, and real-world testbeds are emerging, indicating promising value in applications such as healthcare, wellbeing, and smart environments. For example, care homes are deploying sensorized assisted living platforms to identify the elderly’s daily routines in order to provide personalised care services to them, and smart home technology industries are aiming towards energy-efficient solutions for air purification or heating configurations based on detected human behaviours. One of the core enabling technologies underlying these applications is sensor-based human activity recognition, which consists of inferring high-level activities from low-level sensor data to support context-aware applications. The ability to correctly identify and predict users’ activities underpins the success of these applications.

Studying human behaviours using unobtrusive sensors (including environmental and/or wearable sensors) is a popular research area, and a large number of data- and knowledge-driven techniques have been proposed. However, developing robust human activity recognition systems for long-term and real-world deployments still faces many research challenges, including a lack of high-quality labelled data, continual learning, the emergence of new activities, and privacy issues. This Special Issue serves as a forum to enable researchers and practitioners to present their latest research findings and engineering experiences in empirical studies, including novel techniques  for activity recognition in real-world scenarios.

Dr. Juan Ye
Dr. Gabriele Civitarese
Guest Editors

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