I-Corps: Automatic mood tracker that analyzes facial expressions and emotions against social media activity during social media engagement

这个i-Corps项目的更广泛的影响/商业潜力是开发一种准确的情绪检测技术，以解决社交媒体对年轻成年患者心理健康的影响。根据国家心理健康研究所的数据，18-25岁的年轻人精神疾病的发病率(29.4%)高于26-49岁的成年人(25%)和50岁及以上的成年人(14.1%)。这项拟议的技术将专注于面部识别和面部表情，以使用软件应用程序跟踪情绪。用于检测情绪下降的非接触式传感器可能在健康和健康方面有重要应用。此外，启用面部识别和面部表情等生物识别技术可能会对教育、营销和监控产生重大影响。这项拟议的技术可能会解决从业者和他们的患者的问题，并提供一种影响健康和治疗的工具。这个i-Corps项目基于开发一种自动情绪跟踪器，可以分析社交媒体参与过程中的面部表情和情绪。提出的软件访问最终用户的网络摄像头(在他们的许可下)，并使用情感、人工智能和机器学习算法来分析面部微表情。当一个中性或负面的表情被表达出来时，该软件会截取社交媒体内容的屏幕截图，并将其保存到用户的仪表板上，以进行自我反省。此外，用户的情绪使用面部表情进行实时分析，并在用户的仪表盘上平均作为他们日常情绪的情感分数。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of an accurate mood detection technology to address social media impacts on young adult patients' mental health. According to the National Institute of Mental Health, young adults aged 18-25 years had a higher incidence of mental illness (29.4%) compared to adults aged 26-49 years (25%) and those aged 50 and older (14.1%). The proposed technology will focus on facial recognition and facial expression to track emotions using a software application. Non-contact sensors used to detect mood decline may have significant application to health and wellness. In addition, enabling biometrics such as facial recognition and facial expressions may have significant impact on education, marketing, and surveillance. The proposed technology may address the problem for both the practitioners and their patients and provide a tool for impacting wellness and treatment.This I-Corps project is based on the development of an automatic mood tracker that analyzes facial expressions and emotions during social media engagement. The proposed software accesses the end-users webcam (with their permission) and uses emotion artificial intelligence and machine learning algorithms to analyze facial micro-expressions. When a neutral or negative expression is emoted, the software captures screenshots of social media content and saves it onto the user’s dashboard for self-reflection. In addition, the user’s emotions are analyzed in real time using facial expressions and averaged on the user’s dashboard as an emotional score for their daily mood.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.