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利用附着于小腿的两个传感器获取的步行产生加速度进行步态分析。

Gait Analysis Using Walking-Generated Acceleration Obtained from Two Sensors Attached to the Lower Legs.

作者信息

Saito Ayuko, Sai Natsuki, Kurotaki Kazutoshi, Komatsu Akira, Morichi Shinichiro, Kizawa Satoru

机构信息

Department of Mechanical Science and Engineering, Kogakuin University, 2665-1 Nakanomachi, Hachioji 192-0015, Tokyo, Japan.

Graduate School of Engineering, Kogakuin University, 2665-1 Nakanomachi, Hachioji 192-0015, Tokyo, Japan.

出版信息

Sensors (Basel). 2025 Jul 21;25(14):4527. doi: 10.3390/s25144527.

DOI:10.3390/s25144527
PMID:40732654
Abstract

Gait evaluation approaches using small, lightweight inertial sensors have recently been developed, offering improvements in terms of both portability and usability. However, accelerometer outputs include both the acceleration that is generated by human motion and gravitational acceleration, which changes along with the posture of the body part to which the sensor is attached. This study presents a gait analysis method that uses the gravitational, centrifugal, tangential, and translational accelerations obtained from sensors attached to the lower legs. In this method, each sensor pose is sequentially estimated using sensor fusion to combine data obtained from a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The estimated sensor pose is then used to calculate the gravitational acceleration that is included in each axis of the sensor coordinate system. The centrifugal and tangential accelerations are determined from the gyroscope output. The translational acceleration is then obtained by subtracting the centrifugal, tangential, and gravitational accelerations from the accelerometer output. As a result, the acceleration components contained in the outputs of the accelerometers attached to the lower legs are provided. As only the acceleration components caused by walking motion are captured, thus reflecting their characteristics, it is expected that the developed method can be used for gait evaluation.

摘要

近年来,已开发出使用小型、轻型惯性传感器的步态评估方法,在便携性和可用性方面均有改进。然而,加速度计的输出既包括人体运动产生的加速度,也包括重力加速度,而重力加速度会随着传感器所附着身体部位的姿势而变化。本研究提出了一种步态分析方法,该方法使用从附着在小腿上的传感器获得的重力、离心、切向和平动加速度。在该方法中,利用传感器融合依次估计每个传感器的姿态,以合并从三轴陀螺仪、三轴加速度计和三轴磁力计获得的数据。然后,使用估计出的传感器姿态来计算传感器坐标系各轴中包含的重力加速度。根据陀螺仪输出确定离心加速度和切向加速度。然后,通过从加速度计输出中减去离心加速度、切向加速度和重力加速度来获得平动加速度。结果,得到了附着在小腿上的加速度计输出中包含的加速度分量。由于仅捕捉到由步行运动引起的加速度分量,从而反映了它们的特征,因此预计所开发的方法可用于步态评估。

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本文引用的文献

1
Explaining deep learning models for age-related gait classification based on acceleration time series.基于加速度时间序列解释用于年龄相关步态分类的深度学习模型。
Comput Biol Med. 2025 Jan;184:109338. doi: 10.1016/j.compbiomed.2024.109338. Epub 2024 Nov 12.
2
Human Joint Angle Estimation Using Deep Learning-Based Three-Dimensional Human Pose Estimation for Application in a Real Environment.基于深度学习的三维人体姿态估计的人体关节角度估计及其在真实环境中的应用。
Sensors (Basel). 2024 Jun 13;24(12):3823. doi: 10.3390/s24123823.
3
Longitudinal changes in trunk acceleration and their relationship with gait parameters in post-stroke hemiplegic patients.
脑卒中偏瘫患者躯干加速度的纵向变化及其与步态参数的关系。
Hum Mov Sci. 2024 Feb;93:103176. doi: 10.1016/j.humov.2023.103176. Epub 2023 Dec 30.
4
Orientation Cues-Aware Facial Relationship Representation for Head Pose Estimation via Transformer.基于 Transformer 的面向姿态估计的定向线索感知人脸关系表示。
IEEE Trans Image Process. 2023;32:6289-6302. doi: 10.1109/TIP.2023.3331309. Epub 2023 Nov 20.
5
Comparison of different symmetry indices for the quantification of dynamic joint angles.用于量化动态关节角度的不同对称性指标的比较。
BMC Sports Sci Med Rehabil. 2021 Oct 19;13(1):130. doi: 10.1186/s13102-021-00355-4.
6
Algorithm based on one monocular video delivers highly valid and reliable gait parameters.基于单目视频的算法可提供高度有效且可靠的步态参数。
Sci Rep. 2021 Jul 7;11(1):14065. doi: 10.1038/s41598-021-93530-z.
7
Accuracy of Monocular Two-Dimensional Pose Estimation Compared With a Reference Standard for Kinematic Multiview Analysis: Validation Study.单目二维姿态估计与运动多角度分析参考标准的准确性比较:验证研究。
JMIR Mhealth Uhealth. 2020 Dec 21;8(12):e19608. doi: 10.2196/19608.
8
Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.从后足跑步的胫骨加速度预测步态事件:一种结构化的机器学习方法。
Gait Posture. 2021 Feb;84:87-92. doi: 10.1016/j.gaitpost.2020.10.035. Epub 2020 Nov 10.
9
Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking.基于行走过程中下肢节段角速度的 stance 和 Swing 检测 (注:“stance”和“Swing”在医学步态分析中有特定含义,可分别理解为“站立期”和“摆动期” )
Front Neurorobot. 2019 Jul 24;13:57. doi: 10.3389/fnbot.2019.00057. eCollection 2019.
10
Lower limb angular velocity during walking at various speeds.不同速度行走时的下肢角速度。
Gait Posture. 2018 Sep;65:190-196. doi: 10.1016/j.gaitpost.2018.06.162. Epub 2018 Jun 25.