Wearable Sensors and Signal Processing Technology for Digital Health Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 2055
Special Issue Editors
Interests: cardiovascular measurement system; biomedical signal process; deep learning; machine learning; wearable device for digital health
Special Issues, Collections and Topics in MDPI journals
Interests: medical image and signal processing; cardiac modeling and simulation; medical instrumentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the past decade, wearable devices that can be employed in digital health to measure physiological signals or parameters, such as in electrocardiograms, photoplethysmograms, blood pressure, and the saturation of peripheral oxygen, have been developed to monitor the physical or mental conditions of users in real time. However, these measurement environments are more complex than clinical practices. Moreover, users may be moving or active. Therefore, processing these signals remains challenging. Deep learning and machine learning have proven advantageous in speech processing, and ARM microprocessor cores have advanced significantly. This Special Issue focuses on the application of deep learning or machine learning to process the physiological signals measured by wearable devices and enhance their performance. Moreover, the proposed methods can be implemented on edge devices.
Prof. Dr. Shing-Hong Liu
Prof. Dr. Xin Zhu
Guest Editors
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Keywords
- digital health
- wearable device
- physiological signal and parameter
- deep learning
- machine learning
- edge computing
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