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.

抽象的 行为科学正处于从人类判断转向严格感知的范式转变之中 作为三个独立的 NIH R01（包括）的一部分。 Parent R01 [MH125958]），我们的团队正在开发开源软件工具来精确测量社交 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， 维护和管理大型视听数据集以及伴随它们的无数数据流（例如 例如，可穿戴设备的同步心理生理学记录）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计算的发展 云应用程序所需的计算管道和算法优化。 补充是通过创建一个可供采用的通用数据结构来直接解决这个问题 以及 PC 领域的迭代，以及一组用于处理遵循此结构的数据的基本工具。 本补充文件的母版 R01 是制定此类标准的理想环境，因为它涉及 收集了两个地点的 3000 多个个人视听文件（费城儿童医院） 和贝勒医学院）换句话说，母公司 R01 是所面临的更大挑战的一个缩影。 该领域 - 如何以支持稳健和高度支持的方式有效且无缝地集成单独的数据集 可复制的分析路径。我们的 R01 补充文件将通过开发开源解决方案来应对这一挑战。 传感器数据结构 (SDS) – 供传感器使用的数据生成、存储和基本处理标准 感知计算社区 – 以及开源软件工具和容器环境 我们建议通过 NIH 支持的非常成功的成果来生成和验证数据。 生物医学成像数据结构（BIDS），其开发是为了解决该领域的类似问题 脑成像（例如，需要协调和整理大型多中心数据集）。 配备了收集和分析数据的人员，本补充文件将提供三个新的可交付成果，全部 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） 创建用于实施标准的 Python 模块和容器环境，以及 3) 发布和 在 GitHub 上公开监控这两个可交付成果，这是实现我们开发最终目标的关键一步。 “云就绪”的 PC 工具，并被 PC 社区广泛使用。