Physiological Signal Processing for Wellness

Physiological signals are an invaluable data source that assists in disease detection, rehabilitation, and treatment. These signals represent parameters related to the physiological function of various systems such as the circulatory, neurological, and respiratory systems. They are measured by an array of different sensors attached to the body. Physiological signal processing is a field of research that involves collecting signals from different body parts (e.g., brain, chest, muscles) and analyzing them for disorder and disease detection and diagnostics.

The goal of physiological signal processing for wellness is to monitor body functions, measure the impact of (chronic) illness or trauma as well as the effect of treatment or a healthy lifestyle on physiological functions. It can be used for monitoring physical activity in medical and clinical rehabilitation, sports environments, or as a wellness indicator. The field is rapidly growing, and advanced computational methods, including Artificial Intelligence (deep learning and machine learning), have greatly improved the effectiveness of signal processing and data analysis in this field.

Some of the applications include:

1. Medical Diagnostics: To detect diseases and monitor their progression. For example, using brain, echography, magnetic resonance imaging (MRI), electrocardiogram (ECG), electroencephalogram (EEG), electromyography (EMG) for detection of abnormal activities in the body.

2. Fitness Monitoring: To monitor mental and physical fitness levels. For example, heart rate variability (HRV) can be used to measure an individual's stress levels and physical condition.

3. Brain-Computer Interfaces: To control computers and other devices. For example, EEG and fNIRS signals can be used to control a computer cursor or a robotic arm.

4. Sleep Monitoring: To measure sleep patterns and quality. For example, electrooculogram (EOG) signals can be used to measure eye movements during sleep.

5. Digital Health: Use of digital health platforms can improve access to relevant data, improve care quality, and deliver value to patients, clinicians, and government agencies.

Potential topics for submission to this Research Topic could include, but are not limited to:

• Wearable sensors and devices for physiological signal monitoring
• Signal processing techniques for analyzing and interpreting physiological signals
• Applications of physiological signal processing for disease diagnosis and treatment
• Physiological signal processing for mental health and well-being
• Machine learning and AI approaches for physiological signal processing
• Signal processing for tracking physical fitness and athletic performance
• Use of physiological signals for biofeedback and stress management
• Non-invasive physiological signal monitoring for real-time health monitoring
• Signal processing for sleep analysis and sleep disorders

Overall, this article collection aims to highlight recent advances and encourage interdisciplinary collaboration between researchers and scientists from diverse fields to better understand the human body's response to various stimuli.

Keywords: non-invasive measurements, Medical Diagnostics, Fitness Monitoring, Brain-Computer Interfaces, Sleep Monitoring, MRI, epileptic, electrocardiogram, heart rate variability, EEG, fNIRS, Digital Health, Artificial Intelligence, Deep Learning, Machine Learning, Reinforcement learning, Neural Disorders, Biomedical Engineering, EMG, Health Monitoring, Bioinformatics, Medical Informatics, automatic computer analysis, Clinical Data Processing

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.