Developing algorithms for automated motion sensing

开发自动运动传感算法

• 批准号: 538875-2019
• 负责人: Seamans, Jeremy
• 金额: $ 0.71万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=674626
• 关键词: Developing; algorithms; automated; motion; sensing

XCO is a sport and health sciences company developing hardware and algorithms to provide data for use in sport and health care applications through a motion tracking system based on a hub and multiple radio signal tags. XCO is interested in further developing those algorithms, and new ones, to analyze the characteristics of human motion for sport and medical applications. The application of machine learning techniques, in particular deep learning neural networks, will not only automate costly time-series data integration, but allow more insightful analysis of this data, and improve existing athlete and patient assessment regimes. This project will leverage the data stream analysis and machine learning expertise of Dr. Jeremy Seaman's and his team to accelerate the development of meaningful outputs from XCO's tracking system, providing benefits to analytics and diagnosis in athletics, sports medicine, and clinical neuroscience.

XCO是一家体育和健康科学公司，开发硬件和算法，通过基于集线器和多个无线电信号标签的运动跟踪系统，为体育和医疗保健应用提供数据。XCO有兴趣进一步开发这些算法，以及新的算法，以分析运动和医疗应用中的人体运动特征。机器学习技术的应用，特别是深度学习神经网络，不仅可以自动化昂贵的时间序列数据集成，还可以对这些数据进行更有洞察力的分析，并改善现有的运动员和患者评估制度。该项目将利用Jeremy Seaman博士及其团队的数据流分析和机器学习专业知识，加速XCO跟踪系统的有意义输出的开发，为田径、运动医学和临床神经科学的分析和诊断提供好处。