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Related Concept Videos

Humanistic Psychology01:24

Humanistic Psychology

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Humanistic psychology emerged in the mid-20th century as a response to the deterministic and pessimistic nature of behaviorism and psychoanalysis. While behaviorism focused on observable behaviors influenced by the environment and psychoanalysis delved into unconscious motivations, both theories suggested that human actions lacked free will. In contrast, humanistic psychology offers a perspective that emphasizes the innate potential for goodness and growth within every individual.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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A User-Centric approach for Personalization based on Human Activity Recognition.

Dimitrios G Boucharas, Christos Androutsos, Nikolaos S Tachos

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    Summary
    This summary is machine-generated.

    This study developed a general user profile by integrating behavioral, emotional, medical, and physical patterns. This AI-powered framework offers personalized support and recommendations based on human activity recognition, achieving up to 86.96% accuracy.

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    Area of Science:

    • Human-Computer Interaction
    • Artificial Intelligence
    • Personalized Health Technology

    Background:

    • Existing systems lack comprehensive user profiling integrating diverse data streams.
    • Personalized assistance requires understanding user context, including activities, emotions, and medical history.

    Purpose of the Study:

    • To develop a unified user profile by consolidating behavioral, emotional, medical, and physical data.
    • To create a framework offering personalized functionalities and recommendations based on individual user patterns.
    • To leverage human activity recognition for profile personalization.

    Main Methods:

    • Utilized deep learning techniques for human activity recognition (HAR).
    • Employed a Bayesian belief network for statistical modeling and profile personalization.
    • Developed and validated training and real-time methodological pipelines.

    Main Results:

    • Achieved up to 86.96% accuracy in human activity recognition.
    • Demonstrated the framework's ability to integrate diverse user data into a general profile.
    • Validated the effectiveness of the developed pipelines on multiple datasets.

    Conclusions:

    • The proposed framework successfully integrates multi-modal user data for comprehensive profiling.
    • Personalized assistance and recommendations are feasible through advanced HAR and statistical modeling.
    • The system offers a promising approach for intelligent user support in various environments.