Journal Article

Influence of Sampling Rate on Wearable IMU Orientation Estimation Accuracy for Human Movement Analysis

Du, Mingyu, Cai, Shibo, Fan, Bingfei, Zhang, Luobin, Shull, Peter, Liu, Tao, Li, Qingguo

2025

Overview

Wearable inertial measurement units (IMUs) have been widely used in human movement analysis outside the laboratory. However, the IMU-based orientation estimation remains challenging, particularly in scenarios involving relatively fast movements. Increased sampling rate has the potential to improve accuracy, but it also increases power consumption and computational complexity. The relationship between sampling frequencies and accuracies remains unclear. We thus investigated the specific influence of IMU sampling frequency on orientation estimation across a spectrum of movement speeds and recommended sufficient sampling rates. Seventeen healthy subjects wore IMUs on their thigh, shank, and foot and performed walking (1.2 m/s) and running (2.2 m/s) trials on a treadmill, and a motion testbed with an IMU was used to mimic high-frequency cyclic human movements up to 3.0 Hz. Four widely used IMU sensor fusion algorithms computed orientations at 10, 25, 50, 100, 200, 400, 800, and 1600 Hz and were compared with marker-based optical motion capture (OMC) orientations to determine accuracy. Results suggest that the sufficient IMU sampling rate for walking is 100 Hz, running is 200 Hz, and high-speed cyclic movements is 400 Hz. The accelerometer sampling rate is less important than the gyroscope sampling rate. Further, accelerometer sampling rates exceeding 100 Hz even resulted in decreased accuracy because excessive orientation updates using distorted accelerations and angular velocity introduced more error than merely using angular velocity. These findings could serve as a foundation to inform wearable IMU development or selection across a spectrum of human gait movement speeds.

• Publisher: MDPI AG,MDPI
• Subject: Accelerometers / Accelerometry - methods / Accuracy / Adult / Algorithms / Calibration / Comparative analysis / Electronic data processing / Experiments / Female / Gait / high-rate gyroscope / Human mechanics / Human subjects / Humans / Identification and classification / inertial measurement unit / Inertial measurement units / Magnetic fields / Male / Measurement / Methods / Motion capture / Movement - physiology / Observations / orientation estimation / Running - physiology / sampling rate influence / sensor fusion algorithms / Sensors / strap-down integration / Technology application / Velocity / Walking - physiology / Wearable Electronic Devices / Young Adult