The study of biomechanics during locomotion provides valuable insights into the effects of varying conditions on specific movement patterns. This research focuses on examining the influence of different shoe parameters on walking biomechanics, aiming to understand their impact on gait patterns. To achieve this, various methodologies are explored to estimate human body biomechanics, including computer vision techniques and wearable devices equipped with advanced sensors. Given privacy considerations and the need for robust, accurate measurements, this study employs wearable devices with Inertial Measurement Unit (IMU) sensors. These devices offer a non-invasive, precise, and high-resolution approach to capturing biomechanical data during locomotion. Raw sensor data collected from wearable devices is processed using an Extended Kalman Filter to reduce noise and extract meaningful information. This includes calculating joint angles throughout the gait cycle, enabling a detailed analysis of movement dynamics. The analysis identifies correlations between shoe parameters and key gait characteristics, such as stability, mobility, step time, and propulsion forces. The findings provide deeper insights into how footwear design influences walking efficiency and biomechanics. This study paves the way for advancements in footwear technology and contributes to the development of personalized solutions for enhancing gait performance and mobility.

翻译：运动过程中的生物力学研究为不同条件对特定运动模式的影响提供了宝贵见解。本研究重点考察不同鞋类参数对行走生物力学的影响，旨在理解其对步态模式的作用机制。为实现这一目标，我们探索了多种人体生物力学估计方法，包括计算机视觉技术和搭载先进传感器的穿戴式设备。基于隐私考量及对鲁棒性、精确测量的需求，本研究采用配备惯性测量单元（IMU）传感器的穿戴式设备。该设备通过非侵入式、高精度、高分辨率的方式采集运动过程中的生物力学数据。穿戴设备采集的原始传感器数据通过扩展卡尔曼滤波器进行降噪处理并提取有效信息，包括计算完整步态周期中的关节角度，从而实现对运动动力学的精细化分析。研究揭示了鞋类参数与关键步态特征（如稳定性、移动性、步态时间、推进力）之间的相关性。这些发现为理解鞋履设计如何影响行走效率与生物力学机制提供了更深入的见解。本研究为鞋类技术创新奠定了基础，并为开发提升步态表现与移动能力的个性化解决方案作出了贡献。