Sensor Fusion for Health-tracking Wearable Devices and Internet of Things

用于健康跟踪可穿戴设备和物联网的传感器融合

• 批准号: RGPIN-2017-06558
• 负责人: Park, Edward
• 金额: $ 2.7万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638451
• 关键词: Sensor; Fusion; Health; tracking; Wearable

Great strides have been made within sensor fusion of wearable devices in recent years with the advent of smartphones and smartwatches/bands and their consumer applications. Accuracy and scope for automatic interpretation of a variety of user action detection and classification problems for these devices have improved dramatically. Progress has been driven by new sensor technologies, novel problem definitions and representations, access to volumes of multiple sensor data source types, and the resurgence of powerful signal processing and machine learning algorithms. However, current algorithms lack the capability to produce accurate data that permits detailed health understanding in the real-life setting and transforming such data into meaningful medical outcomes. Health-tracking wearable devices in clinical trials face significant hurdles and challenges that must be overcome in order for them to be adopted and used in healthcare.

近年来，随着智能手机和智能手表/乐队的出现及其消费者应用，近年来，在可穿戴设备的传感器融合中取得了长足的进步。自动解释这些设备的各种用户操作检测和分类问题的准确性和范围已大大提高。进展是由新的传感器技术，新颖的问题定义和表示形式，对多种传感器数据源类型的量的访问以及强大的信号处理和机器学习算法的复兴而驱动的。但是，当前的算法缺乏产生准确数据的能力，该数据允许在现实生活中详细了解健康的理解，并将此类数据转换为有意义的医疗结果。临床试验中的健康跟踪可穿戴设备面临着重大障碍和挑战，必须克服这些挑战，以便在医疗保健中采用和使用它们。