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：“ AddBiomechricic：自动处理和 共享人类运动数据” 抽象的： 与运动有关的伤害和疾病在其寿命的某个时刻影响大多数人。治疗是 很难开发，因为我们预测提出的治疗将如何改变的能力有限 患者的神经肌肉骨骼动力学。机器学习方法可以预测患者对 假设的治疗方法从根本上缩短了新型治疗的发展时间，但我们缺乏 足够的清洁公共数据应用这些方法。生物力学数据太异构，分散， 很小，对于现代机器学习技术有用。 该提案描述了一种新颖的协作方法来创建大型生物力学数据集。主 防止共享生物力学数据的瓶颈是手动处理的非常大的时间成本 数据，缺乏共享的激励措施。我们建议通过基于云的问题解决这两个问题 数据处理自动化工具，如果研究人员同意共享由此产生的，则可以免费使用该工具 去识别数据。 为了证明我们方法的社会生存能力，我们开发了一个原型，以部分自动化 生物力学数据的处理，为研究人员节省了一些时间，他们花费在收集和处理 数据。我们已经将此工具托管为称为AddBiomechranics的云应用程序，如果用户为 愿意共享其上传数据的匿名版本。尽管广告很少，只包括 自2022年7月上旬发射以来，来自130多所大学的研究人员已经使用了一系列最初的功能 处理和共享数据的工具，并且已经集体共享了10,000个运动捕获试验 现在，超过80GB的数据采用统一的，可ML就绪的格式。 我们建议的第一个目的是开发自动化更多生物力学数据处理的方法， 节省研究人员多达90％的时间来收集和处理数据。进一步鼓励 共享数据，我们的第二个目标是改善基于云的工具，为用户提供更多支持，以及 在社区中更广泛地宣传工具。 该项目在生物力学社区中得到了广泛的支持，由 5个国家的8个机构的研究人员，由此产生的数据集将为机器奠定基础 在分析人类运动和通过减少治疗结果的预测中学习突破 共享和汇总运动数据的摩擦。这将增加创新并改善 与运动有关的伤害和疾病，增强了数百万人的生活质量。