Medical Biometrics Enabled Affective Computing (MBEAC)

情感计算是研究和开发能够识别、解释、处理和模拟人类情感的系统的相关领域。医学生物识别指的是生理信号，如心脏信号、皮肤的电皮肤反应和呼吸频率等。这项拟议工作的目标是研究使用医学生物识别技术进行情感计算的方法，该方法可能在医疗保健中应用于精神健康评估、购物和广告中的行为分析、娱乐或教育中的动机特征检测等。在这项拟议的工作中，将使用机器学习技术对人类的情感状态进行建模、检测和分类。利用生物反应产生的信息，申请者和他在多伦多大学的团队将研究和开发揭示情感行为隐藏模式的算法。生物反应是情感最亲密的表达方式。可穿戴技术将首次被用于在现实世界中连续测量生物反应。到目前为止，缺乏适当信号的数据库限制了情感识别技术的任何系统性发展。因此，这项研究的主要目标是：i)研究和开发适合于表征人类情绪并与军事和非军事环境相关的模式识别算法，以及ii)编译适合于评估情感计算算法的适当规模的情感标记生理信号数据库。该数据库将向公众开放，预计将得到在人类情感分类领域进行研究的科学团队的认可。 尽管该领域很重要，尤其是它的潜在应用，但情感分类仍然处于初级阶段，据申请者所知，还没有提出全面的情感计算框架。预计随着满足情感分类任务规格的急需的信号数据库的引入，情感计算领域将填补一个重大空白，并将能够朝着更好和更系统地研究和理解人类情感迈进一步。拟议的技术对加拿大的生活质量和经济的预期好处是显著的。

Affective Computing is the associated field that focuses on the study and development of systems that can recognize, interpret, process and simulate human emotion. Medical biometrics denote physiological signals such as heart signatures, electrodermal responses of the skin and breathing rate, among others. The goal of the proposed work is to research methodologies for affective computing using medical biometrics with potential applications in healthcare for assessment of mental health, behavioral analysis in shopping and advertisement, detection of motivational characteristics in entertainment or education, among others. In the proposed work, human emotional states will be modeled, detected and classified using machine learning technology. Using information that results from biological responses, the most intimate expressions of emotions, the applicant and his team at the University of Toronto will research and develop algorithms that uncover hidden patterns of emotional behavior. For the first time, wearable technology will be used to measure bioresponses continuously in real-world settings. To date, the lack of a database of appropriate signals has limited any systematic development of emotion recognition technologies. Therefore, major objectives of the proposed research are: i) the investigation and development of pattern recognition algorithms suitable to characterize human emotions and be relevant to both military and non-military environments, and ii) the compilation of a suitable-scale database of emotionally-labeled physiological signals that is suitable for the evaluation of affective computing algorithms. The database will be made publicly available and it is expected that it will be endorsed by scientific teams performing research in the field of human emotion classification. Despite the importance of the field, and in particular, its potential applications, emotion classification is still in its infancy and, to the best of the applicant's knowledge, no comprehensive framework has been proposed for affective computing. It is expected that with the introduction of a much needed database of signals that meet the specifications of emotion classification tasks, the field of affective computing will fill a substantial gap and will be able to make steps towards a better and more systematic study and understanding of human emotions. The expected benefits of the proposed technologies on Canadian quality of life and economy are significant.