A Framework for Recognizing and Regulating Emotions in the Elderly

This paper introduces a gerontechnological framework which enables real-time and continuous monitoring of the elderly and provides the best-tailored reactions of a social robot and the proper ambience in order to regulate the older person’s emotions towards a positive emotion. After describing the benefits of the framework for emotion recognition and regulation in the elderly, the eight levels that compose the framework are described. The framework recognizes emotions through studying physiological signals, facial expression and voice. Emotion regulation is enabled by tuning music, color and light to the specific need of the elderly.

[1]  Paulo Novais,et al.  Sensor-driven agenda for intelligent home care of the elderly , 2012, Expert Syst. Appl..

[2]  Marie-Pierre Gleizes,et al.  Deliberative control components for eldercare robot team cooperation , 2015, J. Intell. Fuzzy Syst..

[3]  Antonio Fernández-Caballero,et al.  A Taxonomy of Vision Systems for Ground Mobile Robots , 2014 .

[4]  Katherine B. Martin,et al.  Facial Action Coding System , 2015 .

[5]  James J. Gross,et al.  Emotion Generation and Emotion Regulation: One or Two Depends on Your Point of View , 2011, Emotion review : journal of the International Society for Research on Emotion.

[6]  James J. Gross,et al.  Emotion regulation and psychopathology: A conceptual framework. , 2010 .

[7]  M. Bangert,et al.  Emotion in Motion: Investigating the Time-Course of Emotional Judgments of Musical Stimuli , 2009 .

[8]  L. Lamel,et al.  Emotion detection in task-oriented spoken dialogues , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[9]  Reid G. Simmons,et al.  Affective social robots , 2010, Robotics Auton. Syst..

[10]  G. Dosi.  Sources, Procedures, and Microeconomic Effects of Innovation , 1988 .

[11]  James J Gross,et al.  Bottom-up and top-down emotion generation: implications for emotion regulation. , 2012, Social cognitive and affective neuroscience.

[12]  Gonzalo Bailador,et al.  A Stress-Detection System Based on Physiological Signals and Fuzzy Logic , 2011, IEEE Transactions on Industrial Electronics.

[13]  Lori Lamel,et al.  Annotation and Detection of Emotion in a Task-oriented Human-Human Dialog Corpus , 2007 .

[14]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Davide Carneiro,et al.  A multi-modal approach for activity classification and fall detection , 2014, Int. J. Syst. Sci..

[16]  Alexander J. Rothman,et al.  Emotional states and physical health. , 2000, The American psychologist.

[17]  Hazim Kemal Ekenel,et al.  A common framework for real-time emotion recognition and facial action unit detection , 2011, CVPR 2011 WORKSHOPS.

[18]  Markus A. Maier,et al.  CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE Color and Psychological Functioning , 2022 .

[19]  Johannes Wagner,et al.  Smart sensor integration: A framework for multimodal emotion recognition in real-time , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[20]  Dietmar Winkler,et al.  Bright-Light Therapy in the Treatment of Mood Disorders , 2011, Neuropsychobiology.

[21]  Hosub Lee,et al.  Towards unobtrusive emotion recognition for affective social communication , 2012, 2012 IEEE Consumer Communications and Networking Conference (CCNC).