The Autonomic Nervous System (ANS) branches, Sympathetic Nervous System (SNS) and Parasympathetic Nervous System (PNS), are responsible for the sympatho-vagal balance. It can be attributed to physical and mental state of humans. A number of physiological parameters changes in the short term when humans are challenged with both physically and mentally. These changes in physiological parameters primarily reflect the use of bodily resources up to a certain level, which is justified by the motivation in a given context. Beyond that level, the human state becomes fatigued, and it may result in a chronic form over the long run. Hence the physiological parameters, that reflects the stress/non stress condition should be properly monitored and the mechanism of rehabilitation should be suggested when certain levels are exceeded.
Thanks to the advent of smart sensing, a wide range of physical parameters can be monitored which can be attributed to effort/stress. In situations associated with chronic stress physiological or mental, the SNS can be continuously activated without the normal counteraction of the peripheral nervous system (PNS) which reflect in the changes of cardiac output, pupil dilation and skin resistance, speech spectrum. These physiological markers respond to both physical and mental stress which often make it difficult to distinguish between what has triggered the abnormal condition given the context. While some specific parameters can be studied to classify mental states and physical in a very controlled laboratory settings, it remains a field to further develop sensor-based methods that can classify abnormal the human states with increased sensitivity and specificity.
The core aim of the studies should primarily consider the multi class classification of physical and mental states. Additionally, these studies should suggest tailored rehabilitation strategies based on the insights garnered from sensor data within each distinct context. One of the main challenges in realizing the potential of sensor-based physiological detection is the susceptibility of the collected data to various forms of noise. It is one of the many reasons why the sensor-based physiological detection is still a ‘gimmick’ feature rather than its practical and reliable applications. This can be overcome by fusing a number of sensor data to infer the expected outcomes more reliably. As an example, a motion sensor can be used to identify the level of activity/inactivity. Fusing it with other sensors say, a PPG would make it more accurate if a certain cardiac condition has been triggered by a physically challenging state or not. Again, an elevated speech signature can also help to classify stress condition. Therefore, several physiological measures may be fused to offer a significant classification accuracy in terms of reduced error rates. The precise monitoring and classification of human states have the potential to offer invaluable insights that can pave the way for more efficient therapeutic approaches. This, in turn, has the capacity to significantly elevate overall life quality.
The areas of interest for this Research Topic include, but are not limited to:
• Classification of physical and mental stress;
• Human state classification with wearables;
• Speech features in stress conditions;
• Speech therapy;
• Motion sensor based classification;
• Fall prediction/detection, rehabilitation;
• Balance monitoring;
• Emotion state classification;
• Location tracking for elderly monitoring.
Conflict of interest declaration:
Tanveer Bhuiyan, affiliated with Demant A/S a private company of manufacturing hearing aids.
Raihan Rafique, affiliated with Ericsson, Sweden. A private company for manufacturing wireless communication products and services.
Keywords:
Physical state, mental state, stress, monitoring, smart sensing, physiological markers
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.
The Autonomic Nervous System (ANS) branches, Sympathetic Nervous System (SNS) and Parasympathetic Nervous System (PNS), are responsible for the sympatho-vagal balance. It can be attributed to physical and mental state of humans. A number of physiological parameters changes in the short term when humans are challenged with both physically and mentally. These changes in physiological parameters primarily reflect the use of bodily resources up to a certain level, which is justified by the motivation in a given context. Beyond that level, the human state becomes fatigued, and it may result in a chronic form over the long run. Hence the physiological parameters, that reflects the stress/non stress condition should be properly monitored and the mechanism of rehabilitation should be suggested when certain levels are exceeded.
Thanks to the advent of smart sensing, a wide range of physical parameters can be monitored which can be attributed to effort/stress. In situations associated with chronic stress physiological or mental, the SNS can be continuously activated without the normal counteraction of the peripheral nervous system (PNS) which reflect in the changes of cardiac output, pupil dilation and skin resistance, speech spectrum. These physiological markers respond to both physical and mental stress which often make it difficult to distinguish between what has triggered the abnormal condition given the context. While some specific parameters can be studied to classify mental states and physical in a very controlled laboratory settings, it remains a field to further develop sensor-based methods that can classify abnormal the human states with increased sensitivity and specificity.
The core aim of the studies should primarily consider the multi class classification of physical and mental states. Additionally, these studies should suggest tailored rehabilitation strategies based on the insights garnered from sensor data within each distinct context. One of the main challenges in realizing the potential of sensor-based physiological detection is the susceptibility of the collected data to various forms of noise. It is one of the many reasons why the sensor-based physiological detection is still a ‘gimmick’ feature rather than its practical and reliable applications. This can be overcome by fusing a number of sensor data to infer the expected outcomes more reliably. As an example, a motion sensor can be used to identify the level of activity/inactivity. Fusing it with other sensors say, a PPG would make it more accurate if a certain cardiac condition has been triggered by a physically challenging state or not. Again, an elevated speech signature can also help to classify stress condition. Therefore, several physiological measures may be fused to offer a significant classification accuracy in terms of reduced error rates. The precise monitoring and classification of human states have the potential to offer invaluable insights that can pave the way for more efficient therapeutic approaches. This, in turn, has the capacity to significantly elevate overall life quality.
The areas of interest for this Research Topic include, but are not limited to:
• Classification of physical and mental stress;
• Human state classification with wearables;
• Speech features in stress conditions;
• Speech therapy;
• Motion sensor based classification;
• Fall prediction/detection, rehabilitation;
• Balance monitoring;
• Emotion state classification;
• Location tracking for elderly monitoring.
Conflict of interest declaration:
Tanveer Bhuiyan, affiliated with Demant A/S a private company of manufacturing hearing aids.
Raihan Rafique, affiliated with Ericsson, Sweden. A private company for manufacturing wireless communication products and services.
Keywords:
Physical state, mental state, stress, monitoring, smart sensing, physiological markers
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.