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Manuscript Submission Deadline 28 May 2023

Automatically recognizing affects (which is commonly used as an umbrella term for emotions and moods) will be a vital contribution to mental health care. Especially if negative emotions and conditions such as sadness, depression, stress could be detected automatically, this automatic system could be used as a prescreening tool and could prevent mental disorders by detecting them at an early stage. The rapid development of wearable devices has led to their active use in research on affect recognition. Sensors in wearable devices can collect physiological data, and they enable monitoring and assessment in real-time and in an unobtrusive way. This Research Topic in the Frontiers in Computer Science journal is focused on affective computing methods based on such sensory data. We are inviting original research covering state-of-the-art machine learning approaches and influential applications that can potentially lead to significant advances in this field. The goal is to collect a diverse set of articles on emotion recognition that span across a wide range of sensors, data modalities, their fusion, and classification.

Unobtrusive wearables have emerged as pervasive tools for passive quantitative data collection. More than 330 million fitness trackers, smartwatches and other similar devices are sold and the market is growing every year. Many of these gadgets are capable of measuring physiological data, environmental and activity-related information without disrupting the user's everyday life, unlike the other obtrusive solutions. However, although the performance of wearable devices is improving for recognizing affects, there is still a gap between those and medical-grade devices. More advanced algorithms must be used to overcome artifact problems caused by the unlimited movements especially in the wild. State of the art feature engineering and deep learning techniques should be also applied to improve the performance of such systems.

The scope of this Research Topic includes the latest improvements and technologies regarding "Deep affect monitoring by using wearable sensors" especially on data analysis, analytics, and applications of advanced wearable sensors employed in affective computing. Furthermore, we welcome original research on examining the performance of the state of the art deep learning frameworks on working with the huge amount of sensed data received from multiple wearable sensors.

Keywords: affective computing, deep learning, physiological signal processing, wearable computing, emotion recognition, stress detection


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.

Automatically recognizing affects (which is commonly used as an umbrella term for emotions and moods) will be a vital contribution to mental health care. Especially if negative emotions and conditions such as sadness, depression, stress could be detected automatically, this automatic system could be used as a prescreening tool and could prevent mental disorders by detecting them at an early stage. The rapid development of wearable devices has led to their active use in research on affect recognition. Sensors in wearable devices can collect physiological data, and they enable monitoring and assessment in real-time and in an unobtrusive way. This Research Topic in the Frontiers in Computer Science journal is focused on affective computing methods based on such sensory data. We are inviting original research covering state-of-the-art machine learning approaches and influential applications that can potentially lead to significant advances in this field. The goal is to collect a diverse set of articles on emotion recognition that span across a wide range of sensors, data modalities, their fusion, and classification.

Unobtrusive wearables have emerged as pervasive tools for passive quantitative data collection. More than 330 million fitness trackers, smartwatches and other similar devices are sold and the market is growing every year. Many of these gadgets are capable of measuring physiological data, environmental and activity-related information without disrupting the user's everyday life, unlike the other obtrusive solutions. However, although the performance of wearable devices is improving for recognizing affects, there is still a gap between those and medical-grade devices. More advanced algorithms must be used to overcome artifact problems caused by the unlimited movements especially in the wild. State of the art feature engineering and deep learning techniques should be also applied to improve the performance of such systems.

The scope of this Research Topic includes the latest improvements and technologies regarding "Deep affect monitoring by using wearable sensors" especially on data analysis, analytics, and applications of advanced wearable sensors employed in affective computing. Furthermore, we welcome original research on examining the performance of the state of the art deep learning frameworks on working with the huge amount of sensed data received from multiple wearable sensors.

Keywords: affective computing, deep learning, physiological signal processing, wearable computing, emotion recognition, stress detection


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.

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