Not enough data to create a plot.
Try a different view from the menu above.
Sensory and emotional experiences such as pain and empathy are relevant to mental and physical health. The current drive for automated pain recognition is motivated by a growing number of healthcare requirements and demands for social interaction make it increasingly essential. Despite being a trending area, they have not been explored in great detail. Over the past decades, behavioral science and neuroscience have uncovered mechanisms that explain the manifestations of pain. Recently, also artificial intelligence research has allowed empathic machine learning methods to be approachable. Generally, the purpose of this paper is to review the current developments for computational pain recognition and artificial empathy implementation. Our discussion covers the following topics: How can AI recognize pain from unimodality and multimodality? Is it necessary for AI to be empathic? How can we create an AI agent with proactive and reactive empathy? This article explores the challenges and opportunities of real-world multimodal pain recognition from a psychological, neuroscientific, and artificial intelligence perspective. Finally, we identify possible future implementations of artificial empathy and analyze how humans might benefit from an AI agent equipped with empathy.
Researchers at the University of California San Diego have devised an artificial intelligence (AI) tool to predict the level of loneliness in adults, with 94% accuracy. The tool used Natural Language Processing (NLP) developed by IBM to process large amounts of unstructured natural speech and text data. It analysed factors like cognition, mobility, sleep and physical activity to understand the process of aging. This tool is an example of how AI can be used in devices to detect mental health conditions. Market research firm Gartner predicts, by 2022, your personal device will know more about your emotional state than your own family members.
Ringeval, Fabien, Schuller, Björn, Valstar, Michel, Cummins, NIcholas, Cowie, Roddy, Tavabi, Leili, Schmitt, Maximilian, Alisamir, Sina, Amiriparian, Shahin, Messner, Eva-Maria, Song, Siyang, Liu, Shuo, Zhao, Ziping, Mallol-Ragolta, Adria, Ren, Zhao, Soleymani, Mohammad, Pantic, Maja
The Audio/Visual Emotion Challenge and Workshop (AVEC 2019) "State-of-Mind, Detecting Depression with AI, and Cross-cultural Affect Recognition" is the ninth competition event aimed at the comparison of multimedia processing and machine learning methods for automatic audiovisual health and emotion analysis, with all participants competing strictly under the same conditions. The goal of the Challenge is to provide a common benchmark test set for multimodal information processing and to bring together the health and emotion recognition communities, as well as the audiovisual processing communities, to compare the relative merits of various approaches to health and emotion recognition from real-life data. This paper presents the major novelties introduced this year, the challenge guidelines, the data used, and the performance of the baseline systems on the three proposed tasks: state-of-mind recognition, depression assessment with AI, and cross-cultural affect sensing, respectively.