IMU filter settings for high intensity activities

惯性测量装置 后备箱 运动学 加速度计 加速度 矢状面 滤波器(信号处理) 数学 计算机科学 物理医学与康复 物理 医学 人工智能 计算机视觉 解剖 经典力学 生物 生态学 操作系统
作者
Emily J. Miller,Riley C. Sheehan,Kenton R. Kaufman
出处
期刊:Gait & Posture [Elsevier]
卷期号:91: 26-29 被引量:1
标识
DOI:10.1016/j.gaitpost.2021.10.006
摘要

Recommendations for cut-off frequencies for inertial measurement units (IMU) are either based on marker-based motion analysis or based on low intensity activities. The selection of filter cut-off frequencies can impact the extracted variables from the filtered signal. There are no recommendations for IMU filter settings when collecting biomechanical data of high intensity activities.What are appropriate IMU cut-off frequency filter settings for high intensity activities?Ten unimpaired participants were studied during controlled postural perturbations using a microprocessor-controlled treadmill. Disturbances were delivered in forward and backward directions and incrementally increased in both directions until the participant was unable to maintain an upright posture and the trial resulted in a fall. An IMU was placed on their sternum to obtain trunk sagittal kinematics. Custom code was implemented to estimate trunk angle, angular velocity, and linear acceleration about the flexion-extension axis in the trunk IMU coordinate system. The three trials that resulted in falls in each direction for each participant (60 total trials) were analysed. These trials were limited to 500 msec of the disturbance period. The cut-off frequency was calculated for trunk kinematics using 99 percent of the energy spectrum (E99).The trunk flexion angle (4 ± 4 Hz) and linear acceleration (35 ± 10 Hz) cut-off frequencies agreed with previously reported values. The cut-off frequency for trunk flexion angular velocity (26 ± 7 Hz) was higher than values previously reported.Selection of cut-off frequency should be based on segment accelerations and not simply activity or segment of interest. Deliberate selection and reporting of filter settings in biomechanics research will improve data quality, reliability of inferences, and reproducibility of studies.

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