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: 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=711995
• 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. The long-term goal of the proposed research program is to develop fundamental and principled approaches for fine-grained understanding of complex human activity from multi-modal health-tracking wearable devices for personalized, predictive, preventive, and precision healthcare Internet of Things (IoT). Our short-term goal is to develop automatic activity detection/classification/assessment algorithms for wrist-based inertial wearable devices that are targeted for accurate and continuous health monitoring of free-living older adults (our targeted domain). To achieve this, we propose five specific short-term objectives: (i) sensor fusion for health-tracking wearables: orientation estimation, (ii) physical activity classification for health-tracking wearables: deep learning, (iii) activity-based adaptive/dynamic power management for health-tracking wearables, (iv) physical mobility assessment using health-tracking wearables: walking speed estimation, and (v) physical activity energy expenditure estimation using health-tracking wearables. These algorithms that we develop in this research will enable data-rich and multi-modal health-tracking wearables to produce the necessary data accuracy and interpretation in order for them to be adopted and used in elder care (short-term) and other domains (long-term). We are currently in the very earliest stages of medical grade, health-tracking, advanced wearables and the time frame could be accelerated by the proposed developments. The work proposed will facilitate a new generation of wearables that are reliable and accurate enough for realization of healthcare IoT. It will also enable early inroads for these devices being employed in clinical trials and meeting FDA approvals.

近年来，随着智能手机和智能手表/手环及其消费应用的出现，可穿戴设备的传感器融合取得了长足的进步。这些设备的各种用户操作检测和分类问题的自动解释的准确性和范围已显着提高。新的传感器技术、新颖的问题定义和表示、对多种传感器数据源类型的访问以及强大的信号处理和机器学习算法的复兴推动了进步。然而，当前的算法缺乏生成准确数据的能力，无法生成准确的数据，无法在现实生活中详细了解健康状况，并将这些数据转化为有意义的医疗结果。临床试验中的健康追踪可穿戴设备面临着重大障碍和挑战，必须克服这些障碍和挑战才能在医疗保健领域得到采用和使用。 拟议研究计划的长期目标是开发基本和原则性的方法，通过多模式健康跟踪可穿戴设备对复杂的人类活动进行细粒度的理解，以实现个性化、预测性、预防性和精确的医疗保健物联网（IoT） ）。我们的短期目标是为基于手腕的惯性可穿戴设备开发自动活动检测/分类/评估算法，旨在对自由生活的老年人（我们的目标领域）进行准确和持续的健康监测。为了实现这一目标，我们提出了五个具体的短期目标：（i）用于健康跟踪可穿戴设备的传感器融合：方向估计，（ii）用于健康跟踪可穿戴设备的身体活动分类：深度学习，（iii）基于活动的自适应/健康跟踪可穿戴设备的动态电源管理，(iv) 使用健康跟踪可穿戴设备进行身体移动性评估：步行速度估计，以及 (v) 使用健康跟踪可穿戴设备进行身体活动能量消耗估计。我们在本研究中开发的这些算法将使数据丰富的多模式健康跟踪可穿戴设备能够产生必要的数据准确性和解释，以便它们被采用和用于老年人护理（短期）和其他领域（长期）。 我们目前正处于医疗级、健康追踪、先进可穿戴设备的最早阶段，拟议的开发可能会加快时间框架。拟议的工作将促进新一代可穿戴设备的发展，这些可穿戴设备足够可靠和准确，足以实现医疗保健物联网。它还将使这些设备能够尽早应用于临床试验并满足 FDA 的批准。