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: 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=689319
• 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分析运动表现或评估病理运动模式最普遍的研究方法之一。 我们之前的 Discovery 资助和 Discovery Accelerator 补充奖开发了新方法，通过开发新颖的统计方法和新的软件程序来提高步态运动学数据的准确性和可重复性。 基于这项研究的成功，并基于过去几年可穿戴传感器技术的重大进步，我们现在开始迎接独特的研究挑战，将我们的实验室转移到现实世界并重新定义生物力学步态研究。***加速度计、陀螺仪和磁力计等可穿戴传感器便携且价格实惠，正迅速成为生物力学步态研究的常见替代品。 然而，随着生物力学领域开始采用可穿戴传感器进行研究，需要解决一些限制并建立基础研究。该研究计划的目标是基于新颖的统计方法确保可穿戴传感器数据有效、可靠和可重复。 这里描述的研究计划的总体假设是，在现实世界的数据收集过程中无法控制许多外在因素（例如温度、地形、倾斜度变化等）将显着降低可穿戴传感器的日常可靠性数据并随后影响我们测量有效生物力学步态模式的能力。*** 将通过关注涉及原始和创新统计方法的三个具体目标来评估这一主要假设，并引入新颖的解决方案。 目标 1 将重点开发用于识别步态事件的新方法，目标 2 将重点关注可影响整体分类准确性的新分割和特征提取方法，目标 3 将建立可靠测量步态模式所需的数据会话数量。* ** 这项新颖的研究计划处于融合数据科学和步态生物力学领域的前沿，以分析大量生物力学数据，探索非结构化或复杂的数据集，并开发将产生新见解的预测模型。 我们的科学方法将利用我们由 NSERC 资助的完善的步态分析和可穿戴传感器研究，我们期望开发新的方法，作为未来生物力学研究的基础。 现在，当我们开始新授予的 NSERC CREATE 可穿戴培训和研究合作 (We-TRAC) 培训计划时，当前的发现资助研究计划将确保我们的 HQP 和 NSE 研究人员能够获得最好的工具。 这些工具最终将提高我们在现实环境中使用可穿戴传感器进行步态生物力学研究的进展。 **