Dense life-log health analytics from wearable senors using functional analysis and Riemannian geometry

使用功能分析和黎曼几何对可穿戴传感器进行密集生命日志健康分析

• 批准号: 10023190
• 负责人: Pavan Turaga
• 金额: $ 31.17万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10023190
• 关键词: Accelerometer; Address; Adopted; Algorithmic Software; Algorithms; Behavior monitoring; Behavioral; Big Data; Cellular Phone; Classification; Coupled; Data; Data Analyses; Development; Devices; Diabetes Mellitus; Elements; Generations; Geometry; Goals; Growth; Harvest; Health; Home environment; Hour; Human Activities; Intervention; Life; Location; Machine Learning; Mathematics; Measurement; Modeling; Observational Study; Outcome; Pattern; Periodicity; Physical activity; Process; Regimen; Research; Research Activity; Sampling; Series; Stroke; Techniques; Technology; Time; Work; base; heart rate monitor; improved; innovation; insight; interdisciplinary approach; machine learning algorithm; mathematical algorithm; mathematical sciences; multimodality; sensor; signal processing; statistics; tool; wearable device

The growth and acceptance of wearable devices (e.g., accelerometers) and personal technologies (e.g., smartphones), coupled with larger storage capacities, waterproofing, and more unobtrusive wear locations, has made long-term monitoring of behaviors throughout the 24-hour spectrum more feasible. Wearable devices relevant for human activity (e.g., GENEActiv accelerometer) contain several complementary sensors (accelerometers, gyro, heart- rate monitor etc.) and sample at high rates (e.g., 100Hz for accelerometer). These high-sampling rates and the long duration of capture result in life-log data that truly qualifies as multimodal and big time-series data. The challenges and opportunities involved in fully harvesting these types of data, for widely applicable interventions, suggest that an interdisciplinary approach spanning mathematical sciences, signal processing, and health is needed. Our innovation includes the use of functional-data analysis tools to represent and process the dense time-series data. Functional data analysis is then integrated into machine learning and pattern discovery algorithms for activity classification, prediction of attributes, and discovery of new activity classes. We anticipate that the proposed framework will lead to new insights about human activity and its impact on health outcomes. This interdisciplinary project builds on several research activities of the team. Our past work includes: a) new mathematical developments for computing statistics on time-series data viewed as elements of a function-spaces, b) algorithms for activity recognition that integrate the function-space techniques, and c) data from long-term observational studies of human activity from multimodal sensors. The new work we propose addresses the unique mathematical and computational challenges posed by densely multimodal, long-term, densely-sampled Iifelog big-data in a comprehensive framework. The fusion of ideas from human activity modeling, functional-analysis, geometric metrics, and algorithmic machine learning, present unique opportunities for fundamental advancement of the state-of-the-art in objective measurement and quantification of behavioral markers from wearable devices. The proposed approach also brings to fore: a) new mathematical developments of elastic metrics over multi-modal time-series data, b) comparing sequences evolving on different feature manifolds, c) estimation of quasi- periodicities, d) and a new generation of machine-learning and pattern discovery algorithms. The mathematical and algorithmic tools proposed have the potential to significantly advance how wearable data from contemporary devices with high-sampling rates and large storage capabilities are represented, processed, and transformed into accurate inferences about human activity. Wearable devices are becoming more widely adopted in recent years for general health and recreational uses by the broad populace. This research will result in improved algorithms to process the data available from such wearable devices. The long-term goal of the research is to enable personalized home-based physical activity regimens for conditions such as stroke and diabetes.

可穿戴设备（例如加速度计）和个人技术的增长和接受（例如， 智能手机），再加上更大的存储能力，防水和更多不引人注目的磨损位置 在整个24小时频谱中对行为进行了长期监控。可穿戴设备 与人类活动相关（例如，基因激活加速度计）包含几个互补传感器 （加速度计，陀螺仪，心率监测器等）和较高的样品（例如，加速度计为100Hz）。这些 高采样率和较长的捕获持续时间导致生命数据的数据真正有资格作为多模式和 大时序数据。充分收获这些类型的数据所涉及的挑战和机遇，以广泛收集 适用的干预措施，表明跨学科方法跨越数学科学，信号 需要加工和健康。我们的创新包括使用功能数据分析工具来表示 并处理密集的时间序列数据。然后将功能数据分析集成到机器学习中 用于活动分类，属性预测和发现新活动的模式发现算法 课程。我们预计拟议的框架将导致有关人类活动及其影响的新见解 关于健康结果。这个跨学科项目建立在团队的几项研究活动的基础上。我们过去的工作 包括：a）计算时间序列数据的新数学发展，该数据被视为元素 一个功能空间，b）用于整合功能空间技术的活动识别算法，c）数据 从多模式传感器对人类活动的长期观察研究。我们建议的新作品 解决了密集的多模式，长期的独特数学和计算挑战 在一个全面的框架中，密集采样的iifelog big-data。人类活动的思想融合 建模，功能分析，几何指标和算法机器学习，提出了独特的机会 为了在客观测量和量化行为方面的基本进步 可穿戴设备的标记。提出的方法还带来了：a）新的数学发展 多模式时间序列数据上的弹性指标，b）比较在不同特征上演变的序列 歧管，c）准周期的估计，d）和新一代的机器学习和模式 发现算法。提出的数学和算法工具具有显着的潜力 提高具有高采样率和较大存储功能的现代设备的可穿戴数据 表示，处理并转化为有关人类活动的准确推论。可穿戴设备 近年来，由于广泛的一般健康和娱乐用途，人们变得越来越广泛地采用 民众。这项研究将导致改进的算法来处理此类可穿戴的可用数据 设备。该研究的长期目标是实现个性化的家庭体育锻炼方案 中风和糖尿病等疾病。