IMU Calibration Effect on Lower Limbs Kinematics Against Optical Motion Capture in Post-Stroke Gait

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2025-02-01 DOI:10.1016/j.irbm.2024.100873
Ariane P. Lallès , Geoffroy Moucheboeuf , Emilie Doat , Hélène Pillet , Xavier Bonnet
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引用次数: 0

Abstract

Background

Stroke is the most common cause of disabilities worldwide. Rehabilitation is central to restore functions. Inertial measurement units (IMU) can be used to ease goal settings and monitor progression. Contrary to optical motion capture (OMC), IMU are less expensive, portable, and allow large scale data collections in ambulatory settings. Although Xsens MVN system validity has been demonstrated in healthy participants, its validity among post-stroke (PS) patients is yet to be proven.

Research question

Computation methods being affected by the calibration type; the goal of this study is to compare lower limbs kinematics from Xsens system, after two calibrations against OMC in slow PS walkers exhibiting reduced ranges of movements.

Methods

Data was collected for six PS patients. They were equipped with 29 reflective markers and seven IMU. A minimum of two walks with a dynamic calibration and four walks with a static calibration were performed. All trials were accomplished at a self-selected walking speed and PS used their usual walking aids.

Results

Few interactions between the calibration type and side were found for the ankle abduction/adduction (A/A) bias, root mean square error (RMSE), and range of motion difference (ROMd) (p = 0.011, p = 0.048, p = 0.039). Few effects of the side on errors' values were found. We noticed some effects of the calibration type on errors' values, the dynamic calibration showing better results. In the sagittal plane, we reported RMSE values from 3.6 to 4.8°, 5.2 to 6.5°, and 5.0 to 5.9° for the hip, knee, and ankle dynamic calibration.

Significance

The calibration type, reduced range of movement, and slow walking speed does not seem to impact Xsens' accuracy to a great extent. Nevertheless, dynamic calibration provides slightly better results. Considering the patient's walking ability, we recommend using this calibration.

Abstract Image

IMU标定对脑卒中后步态光学运动捕捉下肢运动学的影响
中风是全世界最常见的致残原因。康复是恢复功能的核心。惯性测量单元(IMU)可以用来简化目标设定和监测进展。与光学运动捕捉(OMC)相反,IMU更便宜,便携,并且允许在流动环境中进行大规模数据收集。虽然Xsens MVN系统的有效性已在健康参与者中得到证实,但其在脑卒中后(PS)患者中的有效性尚未得到证实。研究问题:标定类型对计算方法的影响本研究的目的是比较来自Xsens系统的下肢运动学,经过两次与OMC校准后,缓慢的PS步行者表现出运动范围缩小。方法收集6例PS患者的资料。他们配备了29个反射标记和7个IMU。进行了至少两次动态校准行走和四次静态校准行走。所有的试验都是在自己选择的步行速度下完成的,PS使用他们常用的步行辅助工具。结果踝关节外展/内收(A/A)偏差、均方根误差(RMSE)和活动范围差(ROMd)与校准类型和侧面之间的交互作用较小(p = 0.011, p = 0.048, p = 0.039)。研究发现,侧面对误差值的影响很小。我们注意到标定类型对误差值的影响,动态标定效果更好。在矢状面,我们报告了髋关节、膝关节和踝关节动态校准的RMSE值为3.6至4.8°、5.2至6.5°和5.0至5.9°。校准类型、缩小的运动范围和缓慢的行走速度似乎并没有在很大程度上影响Xsens的准确性。然而,动态校准提供了稍微好一些的结果。考虑到患者的行走能力,我们建议使用此校准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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