Special Issue "Advanced Machine Learning Tools and Methods for IoMT Sensor Applications"
Deadline for manuscript submissions: closed (30 September 2020).
Interests: data stream mining; e-Health applications
Special Issues and Collections in MDPI journals
By 2020, governments are targeting to pave a roadmap for leveraging the latest technologies to empower patients and healthcare workers, to link up sensor devices, to tap into the power of big data and artificial intelligence (AI), etc. This endeavor embraces the latest technologies, including Internet-of-Medical-Things (IoMT), new generations of cloud/fog-computing, AI, and machine learning, which have to be designed in order to satisfy the application requirements of real-time and critical streaming IoMT data.
The rise of IoMT capitalizes on the values of time and space reduction between detection, measurement, and treatment using connected sensors and powerful analytics. While the data feeds received by IoMT come continuously in massive volume and high speed, the capabilities of medical data analytics, machine learning, and AI must keep increasing at a pace faster than before in order to monitor and understand the patterns, context, and meaning of the measurements. Making sound and timely decisions in such healthcare applications is possible when IoMT combined with fast AI can rapidly generate actionable conclusions. This is essential for a wide spectrum of e-Health applications ranging from critical ICU applications to auto-bot telemedicine, medical condition detection, and therapeutic processes.
Sensors can track various critical metrics and alert caregivers to respond in time. Sensors combined with telemedicine make it even easier to help speed up recovery. Knowing what patients are doing in between visits can help to speed up the recovery time for post-surgical procedures. Sensors that track bodily parameters are getting increasingly sophisticated, with blood pressure, glucose levels, sweat, sleep quality, brainwave, and even emotion analysis. IoT infrastructure provides connectedness and logistics in delivering the measurements direct from the sensors instantly to the users and/or doctors. What lacks now is a new breed of AI that can make sense of the multi-modal continuous data streams.
In this Special Issue, research results are needed to advance the current IoMT technologies together with new and fast analytics for providing smarter, wider, quicker patient-oriented e-Health services in the near future.
The latest research breakthroughs, good-quality surveys, and practical use-cases in real-life scenarios are welcomed. Contributions to this Special Issue pertaining—but not limited—to the following are welcome:
- IoMT sensors and architectures;
- IoMT-based e-Health services and applications;
- Cloud and edge computing for IoMT-based e-Health;
- Innovative IoMT devices, instruments, and systems;
- Data stream mining for IoMT-based e-Health;
- Data analytics for IoMT-based e-Health;
- Machine learning and AI for IoMT-based e-Health;
- Ambient assisted living with IoMT;
- Human activity recognition with IoMT;
- IoMT for lifestyle, fitness monitoring, and rehabilitation;
- IoMT for pandemic and epidemiological solutions;
- IoMT decision support systems;
- IoMT data fusion.
Prof. Dr. Simon James Fong
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 2200 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.
- fast data mining and decision supports for IoMT
- machine learning for IoMT
- new IoMT sensors and devices
- IoMT based e-Health applications