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的生命日志健康分析

• 批准号: 9903672
• 负责人: Pavan Turaga
• 金额: $ 31.64万
• 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/9903672
• 关键词: Accelerometer; Activity Cycles; Algorithms; Arrhythmia; Awareness; Behavior; Behavior monitoring; Big Data; Body Temperature; Cellular Phone; Chemotherapy-Oncologic Procedure; Collection; Coupled; Data; Data Analyses; Data Collection; Devices; Dust; Encapsulated; Energy Metabolism; Geometry; Glean; Goals; Growth; Health; Health Status; Healthcare; Heart; Heart Rate; Home environment; Hour; Human; Individual; Lead; Life; Location; Mathematics; Medical; Methods; Modeling; Monitor; Movement; National Institute of General Medical Sciences; Outcome; Participant; Pattern; Periodicity; Physical activity; Population; Research; Research Project Grants; Rest; Sampling; Series; Signal Transduction; Swimming; System; Techniques; Time; United States National Institutes of Health; Vision; Walking; Water; Work; base; circadian; design; diaries; health related quality of life; heart rate monitor; insight; interest; mathematical methods; metastatic colorectal; multimodality; personalized intervention; post stroke; programs; sensor; 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（例如加速计）和个人技术（例如 智能手机），加上更大的存储容量、防水性和更不显眼的佩戴位置， 使得对整个 24 小时范围内的行为进行长期监控变得更加可行。可穿戴设备 与人类活动相关（例如 GENEActiv 加速计）包含多个互补传感器 （加速度计、陀螺仪、心率监测器等）并以高速率采样（例如，加速度计为 100Hz）。这些 高采样率和长时间的捕获导致生命日志数据真正符合多模式和 大时间序列数据。充分收集这些类型的数据所涉及的挑战和机遇，以广泛 适用的干预措施表明，跨数学科学的跨学科方法，信号 需要加工，需要健康。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、属性预测和新活动发现的模式发现算法 类。我们预计拟议的框架将带来关于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多项研究活动的基础上。我们过去的工作 包括：a) 用于计算时间序列数据统计的新数学发展，这些数据被视为 a 函数空间，b) 集成函数空间技术的活动识别算法，以及 c) 数据 来自多模态传感器对人类活动的长期观察研究。我们提出的新工作 解决由密集多模态、长期、 综合框架中的密集采样生活日志大数据。人类活动思想的融合 建模、功能分析、几何度量和算法机器学习提供了独特的机会 从根本上推进行为客观测量和量化方面的最先进技术 来自可穿戴设备的标记。所提出的方法还凸显了：a）新的数学发展 多模态时间序列数据的弹性指标，b）比较在不同特征上演化的序列 流形，c）准周期性估计，d）以及新一代机器学习和模式 发现算法。所提出的数学和算法工具有可能显着 提高现代设备中可穿戴数据的高采样率和大存储能力 被表示、处理并转化为关于人类活动的准确推论。可穿戴设备 近年来，越来越广泛地被广泛应用于一般健康和娱乐用途 民众。这项研究将改进算法来处理此类可穿戴设备中的可用数据 设备。该研究的长期目标是为患者提供个性化的家庭体育活动方案 中风和糖尿病等疾病。