AddBiomechanics: Automatic Processing and Sharing of Human Movement Data

AddBiomechanics：人体运动数据的自动处理和共享

• 批准号: 10743411
• 负责人: Cheng-yun Karen Liu
• 金额: $ 33.43万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10743411
• 关键词: Address; Advertising; Affect; Agreement; Algorithms; Automation; Biomechanics; Cloud Service; Communities; Community Outreach; Computer Vision Systems; Computer software; Consumption; Country; Data; Data Collection; Data Science; Data Set; Databases; Decentralization; Development; Disease; Drops; Electromyography; Engineering; Foundations; Friction; Goals; Human; Image; Incentives; Injury; Institution; Label; Letters; Longevity; Machine Learning; Manuals; Measurement; Methods; Modeling; Modernization; Molecular Biology; Motion; Movement; Musculoskeletal; Newsletter; Optics; Patient-Focused Outcomes; Patients; Persons; Population; Prediction of Response to Therapy; Process; Property; Public Health; Quality of life; Research Personnel; Resources; Running; Sea; Secure; Self-Help Devices; Services; Speed; Techniques; Time; Treatment outcome; Universities; Vendor; Walking; Work; bone; cloud based; collaborative approach; computerized data processing; cost; data de-identification; data sharing; data standards; design; improved; individual patient; innovation; large scale data; machine learning method; member; movement analysis; neuromusculoskeletal; novel; open source; open source tool; patient response; predictive tools; prevent; privacy preservation; prototype; reconstruction; sensor; social; social media; success; tool

Title: “AddBiomechanics: Automatic processing and sharing of human movement data” Abstract: Movement related injuries and disorders affect most people at some point in their lifespan. Treatments are difficult to develop because we have limited ability to predict how a proposed treatment will change the neuromusculoskeletal dynamics of a patient. Machine learning approaches to predict patient responses to hypothetical treatments would radically shorten the development time for novel treatments, but we lack sufficient clean public data to apply these methods. Biomechanics data is too heterogeneous, decentralized, and small to be useful for modern machine learning techniques. This proposal describes a novel collaborative approach to create a large biomechanics dataset. The main bottlenecks preventing the sharing of biomechanics data are the very large time cost to manually process the data, and the lack of incentives for sharing. We propose to address both of these problems with a cloud-based data processing automation tool, which researchers can use for free if they agree to share the resulting de-identified data. To demonstrate the social viability of our approach, we have developed a prototype to partially automate the processing of biomechanics data, saving researchers some of the time they spend collecting and processing data. We have hosted this tool as a cloud application called AddBiomechanics, available for free if users are willing to share anonymized versions of any data they upload. Despite minimal advertising and including only an initial set of features, since our launch in early July 2022 researchers from over 130 universities already use the tool to process and share data, and have already collectively shared 10,000 motion capture trials totaling more than 80GB of data now in a unified, ML-ready format. The first aim of our proposal is to develop methods to automate more of the processing of biomechanics data, saving researchers up to 90% of the time they spend collecting and processing data. To further encourage sharing of data, our second aim is to improve our cloud-based tools, provide more support to our users, and advertise the tools more broadly within the community. This project has broad support in the biomechanics community, evidenced by letters of support from researchers at 8 institutions in 5 countries, and the resulting dataset will lay the foundation for machine learning breakthroughs in the analysis of human movement and prediction of treatment outcomes by reducing friction to share and aggregate movement data. This will increase innovation and improve the treatment of movement related injuries and disorders, enhancing quality of life for millions of people.

标题：《AddBiomMachics：自动处理和 共享人体运动数据“ 摘要： 运动相关的伤害和障碍会影响大多数人一生中的某个阶段。治疗方法是 很难开发，因为我们预测拟议的治疗方法将如何改变 患者的神经肌肉骨骼动力学。预测患者反应的机器学习方法 假设性治疗将从根本上缩短新治疗方法的开发时间，但我们缺乏 有足够的干净的公开数据来应用这些方法。生物力学数据过于异质、分散， 小到对现代机器学习技术有用。 这项建议描述了一种新的协作方法来创建大型生物力学数据集。主 阻碍生物力学数据共享的瓶颈是手动处理的非常大的时间成本 数据，以及缺乏分享的激励。我们建议通过基于云的 数据处理自动化工具，研究人员可以免费使用，如果他们同意分享结果 未识别的数据。 为了展示我们方法的社会可行性，我们开发了一个原型来部分自动化 生物力学数据的处理，为研究人员节省了一些收集和处理数据的时间 数据。我们已将此工具托管为名为AddBiomMachics的云应用程序，如果用户满足以下条件，则可免费使用 愿意分享他们上传的任何数据的匿名版本。尽管广告最少，而且只包括 自我们于2022年7月初推出以来，来自130多所大学的研究人员已经使用了一组初始功能 这款处理和共享数据的工具，已经总共分享了10,000次运动捕捉试验 超过80 GB的数据现在采用统一的、支持ML的格式。 我们建议的第一个目标是开发方法，使更多的生物力学数据处理自动化， 为研究人员节省高达90%的时间来收集和处理数据。为了进一步鼓励 数据共享，我们的第二个目标是改进我们基于云的工具，为我们的用户提供更多支持，以及 在社区内更广泛地宣传这些工具。 这个项目在生物力学领域得到了广泛的支持，来自 来自5个国家8个机构的研究人员，以及由此产生的数据集将为机器奠定基础 在分析人体运动和预测治疗结果方面的学习突破 共享和聚合移动数据的摩擦。这将增加创新并改善对 运动相关的伤害和障碍，提高了数百万人的生活质量。