Artificial Intelligence for Mobile Health
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Wearables".
Deadline for manuscript submissions: closed (16 December 2020) | Viewed by 4164
Special Issue Editors
2. Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3. Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599494, Singapore
4. Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
5. School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD, Australia
Interests: biomedical signal processing; bioimaging; data mining; visualization; biophysics for better health care design; drug delivery and therapy
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; formal methods; biomedical signal processing; service based healthcare; intelligent internet of medical things
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Big data holds the key to unlocking the next level of diagnosis support systems. Big data-based diagnosis support systems are needed to address public health problems, such as cardiovascular disease, fever, obesity, and diabetes. That data comes from sensors that measure physiological signals, such as an electrocardiogram (ECG), heart rate (HR), electroencephalogram (EEG), electromyogram (EMG), and electrooculogram (EOG), from the human body. In order to be diagnostically relevant, the data must be communicated to a central location where it can be processed and accessed by a human expert. Effectively, this is the Internet of Medical Things that allows human experts and artificial intelligence algorithms to work cooperatively on diagnosis and treatment monitoring. Establishing that a symbiotic work relationship has the potential to improve outcomes for patients and reduce the number of years lived with disability.
Today, various machine learning and deep learning techniques have been applied for big data efficiently. Application of such novel methods to the medical data can aid the clinicians to make an accurate and fast diagnosis. Thus, this Special Issue, entitled “Artificial Intelligence for Mobile Health”, focuses on the application of advanced artificial intelligence algorithms, such as machine learning and deep learning techniques, in a mobile health setting.
Prof. U Rajendra Acharya
Dr. Oliver Faust
Guest Editors
Manuscript Submission Information
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Keywords
- Healthcare
- Physiological signals
- Electroencephalography
- Electrooculography
- Electromyography
- Image processing
- Mobile technology
- Deep learning
- Autoencoder
- Convolutional neural network
- Long short-term memory
