AddBiomechanics: Automatic Processing and Sharing of Human Movement Data

标题：“生物力学：自动处理和 共享人类活动数据” 摘要： 与运动相关的损伤和疾病会影响大多数人的一生。治疗是 很难开发，因为我们预测拟议的治疗将如何改变 患者的神经肌肉骨骼动力学。机器学习方法来预测患者对 假设的治疗方法将从根本上缩短新型治疗方法的开发时间，但我们缺乏 有足够的干净的公共数据来应用这些方法。生物力学数据太过异构，分散， 并且小到对现代机器学习技术有用。 该提案描述了一种新的协作方法来创建大型生物力学数据集。主要 阻碍生物力学数据共享的瓶颈是手动处理 数据，以及缺乏分享的激励措施。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 去识别数据。 为了证明我们的方法的社会可行性，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 数据我们将此工具托管为一个名为AddBiomechanics的云应用程序，如果用户 愿意分享他们上传的任何数据的匿名版本尽管广告很少， 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 处理和共享数据的工具，并且已经共同分享了10，000个动作捕捉试验， 超过80 GB的数据现在采用统一的ML就绪格式。 我们提案的第一个目的是开发方法来自动化更多的生物力学数据处理， 节省研究人员收集和处理数据的时间高达90%。进一步鼓励 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， 在社区内更广泛地宣传这些工具。 该项目在生物力学界得到了广泛的支持， 5个国家8个机构的研究人员，由此产生的数据集将为机器学习奠定基础。 在分析人体运动和预测治疗结果方面取得突破， 摩擦来共享和汇总移动数据。这将增加创新，改善 与运动有关的伤害和疾病，提高数百万人的生活质量。

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.