Recent findings have highlighted the importance of ANS correlates as fundamental source of information, in research and clinical practice, for estimation of the (subjects’) psychophysiological (i.e. affective, stress, mood, and behavioural) states. Engineering contribution has been focussing on developing innovative sensing platforms adaptable to different environments and user needs opening new research branches such as smart textile technology, wearable systems, miniaturized electronics and sensors, energy harvesting and so on. It has provided the possibility of gathering information in several scenarios such as during daily activities or sleep, during specific tasks, at home, in lab, and in clinic, in form of physiological signals. The ANS correlates are being considered useful in detecting, analyzing, and modelling psycho-physiological states. Most specific advanced researches have formulated hypotheses on how these states might change or evolve, during normal or pathological cases in a discrete or continuous space of observation. All outcomes in the fields above delineated are obtained by means of a big effort by the most advanced signal-processing techniques, which have played and are playing a crucial role in both embedded and remote implementations. In particular, the most important signal processing contributions could be identified in the use of high level processing techniques, e.g. nonlinear dynamic system theory, innovative methodologies for noise reduction, statistical concepts to determine novel state indexes and characteristics, as well as groundbreaking signal processing paradigms for integrating multivariate information. All theory concepts and experimental outcomes to this area have made possible significant advances in the usage of these ANS correlates to different applications. This Research Topic aims at providing an opportunity for researchers and practitioners interested in applications of ANS correlates to meet and discuss the advances and latest developments in this exciting area.
The main focus of this Research Topic is on the use of advanced signal processing techniques for evaluation and recognition of the Autonomic Nervous System (ANS) correlates in psychophysiological states extended to wearable and smart sensing. Main topics include, but are not limited to:
- Innovative sensing platforms for ANS monitoring
- Smart textile technology
- Wearable systems in healthcare
- Sleep monitor and evaluation through peripheral signals
- Embedded signal processing
- ANS Nonlinear signal processing
- Novel paradigms for ANS characterization
- Artificial Intelligence for psycho-physiological evaluation.
- Data mining for biomedical application
Recent findings have highlighted the importance of ANS correlates as fundamental source of information, in research and clinical practice, for estimation of the (subjects’) psychophysiological (i.e. affective, stress, mood, and behavioural) states. Engineering contribution has been focussing on developing innovative sensing platforms adaptable to different environments and user needs opening new research branches such as smart textile technology, wearable systems, miniaturized electronics and sensors, energy harvesting and so on. It has provided the possibility of gathering information in several scenarios such as during daily activities or sleep, during specific tasks, at home, in lab, and in clinic, in form of physiological signals. The ANS correlates are being considered useful in detecting, analyzing, and modelling psycho-physiological states. Most specific advanced researches have formulated hypotheses on how these states might change or evolve, during normal or pathological cases in a discrete or continuous space of observation. All outcomes in the fields above delineated are obtained by means of a big effort by the most advanced signal-processing techniques, which have played and are playing a crucial role in both embedded and remote implementations. In particular, the most important signal processing contributions could be identified in the use of high level processing techniques, e.g. nonlinear dynamic system theory, innovative methodologies for noise reduction, statistical concepts to determine novel state indexes and characteristics, as well as groundbreaking signal processing paradigms for integrating multivariate information. All theory concepts and experimental outcomes to this area have made possible significant advances in the usage of these ANS correlates to different applications. This Research Topic aims at providing an opportunity for researchers and practitioners interested in applications of ANS correlates to meet and discuss the advances and latest developments in this exciting area.
The main focus of this Research Topic is on the use of advanced signal processing techniques for evaluation and recognition of the Autonomic Nervous System (ANS) correlates in psychophysiological states extended to wearable and smart sensing. Main topics include, but are not limited to:
- Innovative sensing platforms for ANS monitoring
- Smart textile technology
- Wearable systems in healthcare
- Sleep monitor and evaluation through peripheral signals
- Embedded signal processing
- ANS Nonlinear signal processing
- Novel paradigms for ANS characterization
- Artificial Intelligence for psycho-physiological evaluation.
- Data mining for biomedical application