Salminen Mikko, Perttunen Jarmo, Avela Janne, Vehkaoja Antti
Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, 33720, Tampere, Finland.
Faculty of Sports and Health Sciences, Jyväskylä University, Seminaarinkatu 15, 40014, Jyväskylä, Finland.
Heliyon. 2024 Jun 24;10(13):e33546. doi: 10.1016/j.heliyon.2024.e33546. eCollection 2024 Jul 15.
Accurate identification of gait events is crucial to reliable gait analysis. Heel rise, a key event marking the transition from mid-stance to terminal stance, poses challenges in precise detection due to its gradual nature. This leads to variability in accuracy across studies utilizing diverse measuring techniques.
How do different HR detection methods compare when assessed against the underlying heel motion pattern and visual detection across varying speed, footwear conditions, and individuals?
Leveraging data from over 10,000 strides in diverse scenarios with 15 healthy subjects, we evaluated methods based on measurements from optical motion capture (OMC), force plates, and shank-mounted inertial measurement units (IMUs). The evaluation of these methods included an assessment of their precision and consistency with the heel marker's motion pattern and agreement with visually detected heel rise.
OMC-based heel rise detection methods, utilizing the heel marker's vertical acceleration and jerk, consistently identified the same point in the heel motion pattern, outperforming velocity-based methods and our new position-based method resembling traditional footswitch-based heel rise detection. Variability in velocity and position-based methods derives from subtle heel rise variations after mid-stance, exhibiting individual differences. Our proposed IMU-based methods show promise by closely matching OMC-based accuracy.
The results have significant implications for gait analysis, providing insights into heel rise event detection's complexities. Accurate HR identification is crucial for gait phase separation, and our findings, especially with the robust heel marker's jerk-based method, enhance precision and consistency across walking conditions. Moreover, our successful development and validation of IMU-based algorithm offer cost-effective and mobile alternative for HR detection, expanding their potential use in comprehensive gait analysis.
准确识别步态事件对于可靠的步态分析至关重要。足跟抬起是标志着从中期支撑到末期支撑过渡的关键事件，由于其渐进性，在精确检测方面存在挑战。这导致使用不同测量技术的研究在准确性上存在差异。
当根据不同速度、鞋类条件和个体的潜在足跟运动模式及视觉检测进行评估时，不同的足跟抬起检测方法如何比较？
利用15名健康受试者在不同场景下超过10000步的数据，我们基于光学运动捕捉（OMC）、测力板和安装在小腿上的惯性测量单元（IMU）的测量结果对方法进行了评估。对这些方法的评估包括评估它们与足跟标记运动模式的精度和一致性，以及与视觉检测到的足跟抬起的一致性。
基于OMC的足跟抬起检测方法利用足跟标记的垂直加速度和加加速度，始终能在足跟运动模式中识别出同一点，优于基于速度的方法和我们类似于传统基于脚踏开关的足跟抬起检测的新的基于位置的方法。基于速度和位置的方法的差异源于中期支撑后足跟抬起的细微变化，表现出个体差异。我们提出的基于IMU的方法通过与基于OMC的精度紧密匹配显示出前景。
这些结果对步态分析具有重要意义，为足跟抬起事件检测的复杂性提供了见解。准确识别足跟抬起对于步态阶段分离至关重要，我们的发现，特别是基于足跟标记加加速度的稳健方法，提高了不同步行条件下的精度和一致性。此外，我们成功开发并验证了基于IMU的算法，为足跟抬起检测提供了经济高效且可移动的替代方案，扩大了它们在全面步态分析中的潜在用途。
