Methods to improve the reliability of wearable sensor gait data.

提高可穿戴传感器步态数据可靠性的方法。

• 批准号: RGPIN-2019-04374
• 负责人: Ferber, Reed
• 金额: $ 3.35万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=757896
• 关键词: Methods; improve; reliability; wearable; sensor

Biomechanical gait analysis is one of the most ubiquitous research methods for analysing sport performance or evaluating pathologic movement patterns. Our previous Discovery grant, and Discovery Accelerator Supplement award, developed novel methods to improve the accuracy and repeatability of gait kinematic data through the development of novel statistical methods and new software programs. Building on the success of this research, and based on significant advances in wearable sensor technology over the past few years, we now embark on the unique research challenge of moving our laboratory out into the real-world and redefining biomechanical gait research.     Wearable sensors, such as accelerometers, gyroscopes, and magnetometers, are portable and affordable and are quickly becoming a common alternative for biomechanics gait research. However, as the field of biomechanics begins to embrace the use of wearable sensors for research purposes, several limitations needs to be addressed and foundational research must be established. The objective of this research program is to ensure that wearable sensor data are valid, reliable and repeatable based on novel statistical methods. The overarching hypothesis of the research program described here is that the inability to control many extrinsic factors (e.g. temperature, terrain, changes in inclination, etc.) during real-world data collections will significantly reduce the day-to-day reliability of wearable sensor data and subsequently affect our ability to measure valid biomechanical gait patterns.     This main hypothesis will be evaluated, and novel solutions introduced, by focusing on three Specific Aims involving original and innovative statistical methods. Aim 1 will focus on developing new methods for identifying gait events, Aim 2 will focus on new segmentation and feature extraction methods that can influence overall classification accuracy, and Aim 3 will establish the number of data sessions necessary for reliable measurements of gait patterns.     This novel research program is at the forefront of merging the fields of data science and gait biomechanics to analyze large quantities of biomechanical data, explore unstructured or complex data sets, and develop prediction models that will produce new insights. Our scientific approach will capitalize on our well-established NSERC-funded gait analysis and wearable sensor research and we expect to develop novel methods that will serve as the foundation for future biomechanics research. Now as we begin our newly awarded NSERC CREATE Wearable Training and Research Collaboration (We-TRAC) training program, the current Discovery Grant research program serves to ensure that our HQP and NSE researchers have access to the best tools. These tools will ultimately improve our progress in using wearable sensors for gait biomechanics research in real-world settings.

生物力学步态分析是最普遍存在的堡垒表现或评估病理运动的。在一年中，我们呼吁将我们的实验室带来现实世界中的独特研究挑战，并重新定义生物力学步态研究，例如加速度计，陀螺仪和磁力计y vecommeters y vecommeters目的，要添加的严重性局限传感器数据并随后会评估有效的生物力学步态模式。将建立步态模式的可靠测量所必需的颈部会话，探索非结构化的或复杂的数据集，并开发预测模型，以产生我们的科学方法。以及可穿戴的传感器研究，我们希望开发新的方法，这些方法将成为未来生物力学研究的基础。研究人员可以使用最佳工具。