Abstract
Goal: Gait monitoring is useful for diagnosing movement disorders or assessing surgical outcomes. This paper presents an algorithm for estimating pelvis, thigh, shank, and foot kinematics during walking using only two or three wearable inertial sensors. Methods: The algorithm makes novel use of a Lie-group-based extended Kalman filter. The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero-velocity update, and flat-floor assumption), and constraint update (hinged knee and ankle joints, constant leg lengths). Results: The inertial motion capture algorithm was extensively evaluated on two datasets showing its performance against two standard benchmark approaches in optical motion capture (i.e., plug-in gait (commonly used in gait analysis) and a kinematic fit (commonly used in animation, robotics, and musculoskeleton simulation)), giving insight into the similarity and differences between the said approaches used in different application areas. The overall mean body segment position (relative to mid-pelvis origin) and orientation error magnitude of our algorithm ( n=14 participants) for free walking was 5.93 ± 1.33 cm and 13.43 ±1.89 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> when using three IMUs placed on the feet and pelvis, and 6.35 ± 1.20 cm and 12.71 ±1.60 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">°</sup> when using only two IMUs placed on the feet. Conclusion: The algorithm was able to track the joint angles in the sagittal plane for straight walking well, but requires improvement for unscripted movements (e.g., turning around, side steps), especially for dynamic movements or when considering clinical applications. Significance: This work has brought us closer to comprehensive remote gait monitoring using IMUs on the shoes. The low computational cost also suggests that it can be used in real-time with gait assistive devices.
Highlights
The tracking of human body movement has fascinated researchers for years, but has recently found application in robotics, virtual reality, animation, and healthcare
L7S3I’s epos is 0.5 cm better than L7S-2I, which is expected as L7S-3I utilises more inertial measurements units (IMUs) sensor units
The results for dynamic movements were worse, notably for the TCD dataset (∆7 cm, ∆11◦, compared to free walking results), which was expected as the TCD dataset contains movements that break our pelvis pseudo-measurement assumptions
Summary
The tracking of human body movement has fascinated researchers for years, but has recently found application in robotics, virtual reality, animation, and healthcare (e.g., gait analysis). Direct kinematic analysis involves estimating pose (i.e., position and orientation of body segments) directly from the markers (e.g., Vicon’s Plug-in Gait) [1]. It is typically used in gait analysis. Inverse kinematics estimates the best skeletal pose by optimising the pose of a linked-segment model of the skeleton to best match the captured OMC marker data. It is typically used in robotics, animation, and in musculoskeletal modelling software (e.g., OpenSim) [3]. The fact remains that each approach has limitations, and the most appropriate model may depend on the application
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