Sensor-Based Human Activity Recognition in Real-World Scenarios
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 April 2022) | Viewed by 37232
Special Issue Editors
Interests: human activity recognition; sensor data analysis, smart environments; context awareness; uncertainty reasoning; temporal reasoning; ontologies; mobile computing; wearable assistive technologies; multi-sensory integration
Special Issues, Collections and Topics in MDPI journals
Interests: context awareness; sensor-based human activity recognition; smart homes; wearable systems; hybrid data-driven and knowledge-based reasoning methods for pervasive computing
Special Issue Information
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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Keywords
- semi-supervised learning
- weakly supervised learning
- unsupervised learning
- collaborative learning
- federated learning
- continual learning
- data augmentation for activity recognition
- transfer learning
- privacy-aware activity recognition
- knowledge-based reasoning
- hybrid knowledge- and data-driven activity recognition
- novel sensing technologies for activity recognition
- novel datasets for activity recognition
- novel applications for activity recognition
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