A Thermal-Optical Vision System for Identification and Localization of Symptomatic COVID-19 Patients

用于识别和定位有症状的 COVID-19 患者的热光视觉系统

• 批准号: 554496-2020
• 负责人: JanabiSharifi, Farrokh
• 金额: $ 3.64万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705664
• 关键词: Thermal; Optical; Vision; System; Identification

According to World Health Organization (WHO) officials, timely identification and isolation of individuals infected with coronavirus is the key to containing the spread of the virus. With a shortage of testing kits, laboratory supplies, and personnel, temperature screening remains the primary approach to identifying high risk individuals. Thermal screening is non-invasive and gives immediate results for one of the primary symptoms of COVID-19. However, current methods of COVID-19 public screening in 'high traffic' environments involve handheld temperature measurements which are considered inefficient, ineffective, and potentially risky to the screener and other acutely ill patients. Relying on only elevated skin temperatures (and not core temperatures) using distance measurement units might also give false positive classification results. It is hypothesized that fusing temperature measurements with body gait patterns (markers) would increase the accuracy of identifying symptomatic COVID -19 patients. In this project, researchers from Ryerson University are partnering with Altius Analytics Labs to enhance the scalability and efficiency of COVID-19 public screening and disease tracking. The main goal of the project is to develop, validate, and test a novel thermal-optical vision-based system to automatically identify symptomatic individuals on a large scale and track their movement through an environment. The outcome of this project will have a significant impact, protecting public health for the current pandemic and providing safeguards against a potential second wave. In addition to medical screening applications, results can be extended to the human factors industry as well. Finally, the project includes significant multidisciplinary training of highly qualified personnel (HQP) in established and emerging areas, including kinesiology, instrumentation, machine vision, signal processing, and machine learning. HQP will also have opportunities to work with the project partner and gain career relevant industrial experience.

根据世界卫生组织（WHO）官员的说法，及时识别和隔离感染冠状病毒的人是遏制病毒传播的关键。由于缺乏测试套件，实验室用品和人员，温度筛查仍然是确定高风险个人的主要方法。热筛选是无创的，可立即为Covid-19的主要症状之一提供结果。但是，在“高流量”环境中共同筛查的当前方法涉及手持式温度测量值，这些测量值被认为对筛查者和其他急性病患者被认为无效，无效且可能存在风险。仅使用距离测量单元仅依靠升高的皮肤温度（而不是核心温度），也可能给出假阳性分类结果。假设与身体步态模式（标记）融合温度测量值将提高识别有症状的Covid -19患者的准确性。 在这个项目中，瑞尔森大学的研究人员正在与Altius Analytics Labs合作，以提高Covid-19-19公共筛查和疾病跟踪的可扩展性和效率。该项目的主要目标是开发，验证和测试一种新型的基于热光学视觉的系统，以自动识别有症状的人，并通过环境跟踪他们的运动。该项目的结果将产生重大影响，保护当前大流行的公共卫生，并为潜在的第二波提供保障措施。除了医疗筛查应用外，结果还可以扩展到人为因素行业。最后，该项目包括对建立和新兴领域中高素质人员（HQP）的重大多学科培训，包括运动机能学，仪器，机器视觉，信号处理和机器学习。 HQP还将有机会与项目合作伙伴合作并获得与职业相关的工业经验。