Medical Biometrics Enabled Affective Computing (MBEAC)

情感计算是一个相关领域，专注于研究和开发能够识别、解释、处理和模拟人类情感的系统。医学生物识别技术表示诸如心脏特征、皮肤电反应和呼吸频率等生理信号。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，这些方法在医疗保健方面有潜在的应用，包括心理健康评估、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。在提出的工作中，人类的情绪状态将使用机器学习技术进行建模、检测和分类。申请人和他在多伦多大学的团队将利用生物反应（情感最亲密的表达）产生的信息，研究和开发揭示情感行为隐藏模式的算法。可穿戴技术将首次被用于在现实环境中连续测量生物反应。迄今为止，缺乏适当的信号数据库限制了情感识别技术的系统发展。因此，本研究的主要目标是：1)研究和开发适合于描述人类情绪并与军事和非军事环境相关的模式识别算法；2)编制适合于评估情感计算算法的适当规模的情绪标记生理信号数据库。该数据库将向公众开放，预计它将得到在人类情感分类领域进行研究的科学团队的认可。

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.