Medical Biometrics Enabled Affective Computing (MBEAC)

情感计算是一个相关的领域，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。医学生物特征表示生理信号，例如心脏签名、皮肤的皮肤电反应和呼吸率等。拟议的工作的目标是研究方法的情感计算使用医疗生物识别技术与潜在的应用在医疗保健的心理健康评估，行为分析在购物和广告，在娱乐或教育中的动机特征的检测，等等。在拟议的工作中，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（最亲密的情感表达）产生的信息，研究和开发揭示隐藏的情感行为模式的算法。可穿戴技术将首次用于在现实环境中连续测量生物反应。迄今为止，缺乏适当信号的数据库限制了情感识别技术的任何系统发展。因此，建议的研究的主要目标是：i）适合于描述人类情感的模式识别算法的研究和开发，并与军事和非军事环境相关，以及ii） 编制一个适当规模的数据库的情感标记的生理信号，这是适合于情感计算算法的评估。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。 尽管该领域的重要性，特别是其潜在的应用，情感分类仍然处于起步阶段，并且据申请人所知，还没有提出用于情感计算的全面框架。预计随着满足情感分类任务规范的信号的急需数据库的引入，情感计算领域将填补一个巨大的空白，并将能够朝着更好和更系统地研究和理解人类情感的方向迈出步伐。拟议技术对加拿大生活质量和经济的预期效益是显著的。

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.