Special Issue "Activity Recognition Using Constrained IoT Devices"
Deadline for manuscript submissions: 15 July 2021.
Interests: wearable computing; activity recognition; digital health; unobtrusive sensing
Interests: wearable, ubiquitous, and mobile computing; artificial intelligence; data mining; digital health
Special Issues and Collections in MDPI journals
Special Issue in Applied Sciences: Ubiquitous Technologies for Emotion Recognition
Special Issue in International Journal of Environmental Research and Public Health: Intelligent IoT-Based E-health Systems for a Higher Inclusion of Vulnerable People
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
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- behavior modelling
- unobtrusive sensing
- activity recognition
- constrained devices
- resource constrained environments
- privacy by design
- edge computing
- deep learning
- transfer learning
- ethical issues
- context awareness
- wearable computing
- pervasive computing