Machine Learning in MEMS for Biomarkers Generation

MEMS 中的机器学习用于生成生物标志物

• 批准号: 568675-2021
• 负责人: Sylvestre, Julien
• 金额: $ 8.3万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=722400
• 关键词: Machine; Learning; MEMS; Biomarkers; Generation

The goal of the proposed project is to integrate the new AIMEMS sensor technology with wearable medical device to improve the generation of digital health biomarkers. AIMEMS form an entirely new class of MEMS, where AI capabilities are built directly into the dynamics of the MEMS. This tight integration of AI into physical devices allows the fabrication of compact and energy efficient sensors, that can be taught using standard machine learning algorithms to realize complex data filters, classifiers or other computational tasks. The principal new concept in the proposed research is to use the AIMEMS machine learning capabilities 'at the edge' of a digital biomarker data processing system. These biomarkers are physiological and behavioral measures collected through connected digital tools to explain, influence or predict health-related outcomes. The outcomes of the proposed project will comprise new or improved digital biomarkers with a strong potential to generate medical breakthroughs for the diagnosis and management of viral respiratory diseases (such as COVID-19 and Influenza), and of type 2-Diabetes Mellitus with cardiovascular diseases as its principal comorbidity. The research activities will be organized along two mostly parallel tracks: one that uses an AIMEMS microphone to identify and classify different acoustic signatures from respiratory infections, and one that uses an AIMEMS accelerometer to improve the efficiency of a wearable digital biomarker system for the screening of at-risk individuals, to promote the diagnosis and management of type 2-Diabetes Mellitus and cardiovascular diseases.

该项目的目标是将新的AIMEMS传感器技术与可穿戴医疗设备集成，以改善数字健康生物标志物的生成。AIMEMS形成了一个全新的MEMS类别，其中AI功能直接内置到MEMS的动态中。这种将人工智能与物理设备的紧密集成允许制造紧凑且节能的传感器，可以使用标准的机器学习算法来教授这些传感器，以实现复杂的数据过滤器、分类器或其他计算任务。拟议研究中的主要新概念是在数字生物标志物数据处理系统的“边缘”使用AIMEMS机器学习能力。这些生物标志物是通过连接的数字工具收集的生理和行为测量，用于解释、影响或预测与健康相关的结果。拟议项目的成果将包括新的或改进的数字生物标志物，这些标志物具有强大的潜力，可为病毒性呼吸道疾病（如COVID-19和流感）以及以心血管疾病为主要合并症的2型糖尿病的诊断和管理带来医学突破。研究活动将沿着两个基本平行的方向组织：一个是使用AIMEMS麦克风识别和分类来自呼吸道感染的不同声学特征，另一个是使用AIMEMS加速度计提高可穿戴数字生物标志物系统的效率，用于筛查高危人群，促进2型糖尿病和心血管疾病的诊断和管理。