Low-Cost Sensors and Biological Signals

Dear Colleagues,

The electrical, chemical, and mechanical activities that occur during various biological events produce signals that can be measured by sensors, followed by storage and subsequent analysis. These biosignals contain valuable information that can be used to understand the underlying physiological mechanisms of specific biological functions, such as blood pressure, body temperature, joint movement, and the electrical activity of the brain, heart, and muscles. Thus, they may be crucial for clinicians and medical diagnosis.

In the last few years, a great variety of sensors dedicated to biosignals have become available at prices typically lower than 100 USD. These sensors are known as “low-cost sensors”, in contrast to the gold-standard materials used in clinical environments and research centers. For example, movement of a joint can be tracked using low-cost 3D cameras, such as Microsoft’s Kinect v2. The use of such sensors in daily clinical practice may greatly favor the collection of big data and allow broader diffusion of evidence-based medicine, which is essential to improve medical practice. Low-cost sensors may also be of interest in virtual- or augmented-reality medical and rehabilitation applications.

However, the use of low-cost sensors is associated with several challenges. Firstly, sensors should be accurate enough to unambiguously compute relevant indicators from biosignals, in particular, in patients with medical conditions. Secondly, the designed sensors should be as non-intrusive and ready-to-use as possible with fast calibration procedures. Third, they require user-friendly and cross-platform interfaces that provide secure data storage and easy data analysis and visualization.

Authors are invited to submit articles to this Special Issue of Sensors “Low-Cost Sensors and Biological Signals” on, but not limited to, the following topics:

• Clinical applications of low-cost sensors;
• Metrological comparison between low-cost sensors and gold-standard sensors;
• Calibration methods;
• Signal processing (including deep-learning techniques);
• Data storage and/or wireless transmission;
• Real-time data processing and visualisation;
• Use of sensors in rehabilitation (biofeedback, virtual reality, augmented reality, etc.);
• Sensor design and noninvasive measurement techniques;
• Ethical and epistemological dimensions of sensor-based medicine.

The Guest Editors thank the full support from the European Regional Development Fund (Interreg FWVl NOMADe) so as to be guest editors of this special issue, especially the financial support of some publications in this special issue.

Dr. Frédéric Dierick
Dr. Fabien Buisseret
Dr. Stéphanie Eggermont
Guest Editors

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