Enhancing the Cloud-Readiness of Perceptual Computing Through Data Standardization Software

通过数据标准化软件增强感知计算的云就绪性

• 批准号: 10609245
• 负责人: JOHN David HERRINGTON
• 金额: $ 26.02万
• 依托单位: CHILDREN'S HOSP OF PHILADELPHIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-28 至 2023-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10609245
• 关键词: Achievement; Address; Adoption; Algorithms; Architecture; Artificial Intelligence; Behavioral Sciences; Brain imaging; Collaborations; Collection; Communities; Computational algorithm; Computer Vision Systems; Computer software; Data; Data Analyses; Data Files; Data Set; Databases; Development; Docking; Environment; Face; Generations; Goals; Heart Rate; Human; Individual; Industry; Investments; Judgment; Language; Measures; Medicine; Metadata; Monitor; Movement; Output; Parents; Participant; Pediatric Hospitals; Philadelphia; Predictive Analytics; Psychophysiology; Pythons; Readiness; Report (document); Research; Research Personnel; Series; Site; Social Behavior; Software Engineering; Software Tools; Specific qualifier value; Structure; Testing; United States National Institutes of Health; Visual; biomedical imaging; cloud based; college; computational pipelines; data format; data repository; data standards; data streams; data structure; emotional behavior; experience; flexibility; image processing; improved; innovation; member; open source; repository; sensor; success; tool; wearable device

ABSTRACT Behavioral science is in the midst of a paradigm shift away fromhuman judgment and toward rigorous perceptual computing (PC) and investment in human predictive analytics. As a part of three separate NIH R01s (including the Parent R01 [MH125958]), our team is developing open-source software tools to exquisitely measure social and emotional behavior, including tools to quantify facial movements, body actions, and language. Unfortunately, the field of perceptual computing currently has no agreed-upon standard for organizing, maintaining, and curating large audio-visual datasets, and the myriad data streams that accompany them (for example, simultaneous psychophysiology recordings from wearable devices). The absence of such a standard is a major impediment to innovation in perceptual computing, as it hinders the development of large -scale computational pipelines and algorithm optimizations that cloud applications require. The purpose of this Supplement is to directly address this problemthough the creation of acommon data structure ready for adoption and iteration by the field of PC, along with a set of basic tools for working with data that follow this structure. The parent R01 for this Supplement is an ideal context in which to develop such a standard, as it involves the collection of more than 3000 individual audio-visual files across two sites (The Children’s Hospital of Philadelphia and Baylor College of Medicine). In other words, the parent R01 is a microcosm of the larger challenge facing the field – how to effectively and seamlessly integrate separate datasets in ways that supports robust and highly replicable analysis paths. This Supplement to our R01 will address this challenge by developing an open-source Sensor Data Structure (SDS) – a data generation, storage, and basic processing standard for use by the perceptual computing community – along with open-source software tools and Container environments to generate and validate data. We propose to parallel the achievements by highly successful NIH -supported Biomedical Imaging Data Structure (BIDS), which was developed to address an analogous problem in the field of brain imaging (e.g., the need to harmonize and curate large multicenter datasets). Although the Parent R01 is staffed for collecting and analyzing data, this Supplement would provide three new deliverables, all implemented via the addition of software engineer with industry experience: 1) creation of the data structure, 2) creation of a Python module and Container environment for implementing the standard, and 3) posting and monitoring these two deliverables publicly on GitHub. This is acritical step toward our ultimate goal of developing PC tools that are “cloud-ready”, and in widespread use by the PC community.

摘要 行为科学正处于从人类判断转向严格感性的范式转变之中 计算（PC）和投资人类预测分析。作为三个独立NIH R 01的一部分（包括 Parent R 01 [MH 125958]），我们的团队正在开发开源软件工具， 和情绪行为，包括量化面部动作，身体动作和语言的工具。 不幸的是，感知计算领域目前还没有统一的组织标准， 维护和策划大型视听数据集，以及伴随它们的无数数据流（用于 例如，来自可穿戴设备的同时心理生理学记录）。缺乏这样的标准 是感知计算创新的主要障碍，因为它阻碍了大规模 云应用程序所需的计算管道和算法优化。这样做的目的 补充的是直接解决这个问题，通过创建通用的数据结构，以备采用 和PC领域的迭代，沿着一组基本工具，用于处理遵循这种结构的数据。的 本补充文件的父R 01是制定此类标准的理想环境，因为它涉及 收集了两个站点（费城儿童医院）的3000多个单独的视听文件 贝勒医学院（Baylor College of Medicine）换句话说，父R 01是所面临的更大挑战的缩影 该领域-如何有效地和无缝地集成独立的数据集的方式，支持强大的，高度 可复制的分析路径。我们的R 01的补充将通过开发一个开源的 传感器数据结构（SDS）--一种数据生成、存储和基本处理标准， 感知计算社区-沿着开源软件工具和容器环境， 生成和验证数据。我们建议将非常成功的NIH支持的成就与之相媲美。 生物医学成像数据结构（BIDS），这是为了解决该领域的类似问题而开发的 脑成像（例如，协调和管理大型多中心数据集的需求）。虽然R 01 的工作人员负责收集和分析数据，本补编将提供三个新的可交付成果， 通过增加具有行业经验的软件工程师来实施：1）创建数据结构，2） 创建一个Python模块和容器环境来实现标准，以及3）发布和 在GitHub上公开监控这两个可交付成果。这是朝着我们发展的最终目标迈出的关键一步。 PC工具是“云就绪”，并在PC社区广泛使用。