Springer Studies in Computational Intelligence: 

Book Series (Peer reviewed)

Please be informed that the accepted chapters will be indexed in Scopus.

Based on multiple requests, the deadline has been extended to April 30, 2024. 

Nevertheless, full contributions already submitted will undergo the reviewing process.

Call for contribution

Active and Assisted Living (AAL) has evolved dramatically in the last decades. AAL focuses on supporting people, especially people with special needs (elderly or people affected by particular medical pathologies), in their daily activities and environments, aiming to increase their quality of life. However, the support is not limited to people with special needs or the elderly but includes their relatives, social support services, health specialists, and caregivers. Usually, the support process targets the person's physical, mental or emotional state. Such a combination may lead to different support solutions integrated with the smart environment.

Supporting people's autonomy requires modeling the environment and the spatial-temporal context of the people living in it. Therefore, the context should be represented using a formal knowledge representation approach, e.g., ontologies or conceptual models, that can offer a flexible possibility to apply reasoning approaches to decide the optimal support services. Different reasoning approaches can be applied based on graphical models, e.g., hidden Markov models or Bayesian networks, syntactical models, e.g., fuzzy logic or roughest theory, and logic-based reasoning, e.g., answer set programming. After defining the contextual model, the recognized activities and the emotional status is fed into the contextual model to enable the reasoning process. Extensive research has been accomplished on human activity and emotion recognition based on classical machine learning and deep learning models. Nevertheless, a limited number of works combine research fields with the person's context, aiming to provide optimal support for smart environments. Moreover, recognizing human activities can be divided into two major research fields: human actions recognition related to simple actions based on visual, non-visual, or multimodal sensors, and human complex activities recognition formed by combinations of human actions.  Furthermore, human emotion recognition uses intrusive physiological sensors, like Electroencephalography (EEG) and Electrocardiography (ECG) sensors, semi-intrusive sensors like Electrodermal Activity (EDA) or Skin Temperature (ST) sensors, or non-intrusive sensors like cameras or 3D scanner sensors. 

The book considers five major research fields in AAL, context modeling, human activity recognition, human emotion recognition, human support, and sensors technology:

The book will attract researchers, students, or lecturers to cover the state-of-the-art approaches, techniques, and methodologies in human activity and emotion recognition w.r.t. support in smart environments.  

Book Editors