Abstract
Biomechanical analysis of human movement is based on dynamic measurements of reference points on the subject’s body and orientation measurements of body segments. Collected data include positions’ measurement, in a three-dimensional space. Signal enhancement by proper filtering is often recommended. Velocity and acceleration signal must be obtained from position/angular measurement records, needing numerical processing effort. In this paper, we propose a comparative filtering method study procedure, based on measurement uncertainty related parameters’ set, based upon simulated and experimental signals. The final aim is to propose guidelines to optimize dynamic biomechanical measurement, considering the measurement uncertainty contribution due to the processing method. Performance of the considered methods are examined and compared with an analytical signal, considering both stationary and transient conditions. Finally, four experimental test cases are evaluated at best filtering conditions for measurement uncertainty contributions.
Highlights
The biomechanical study of human movement requires a strict integration between experimental data and models to describe motion patterns [1,2]
A parameter parameter can can error be selected according to the objectives of the biomechanical analysis that is carried out be selected according to the objectives of the biomechanical analysis that is carried out and an estimation of the possible error due to the differentiation/filtering and an estimation of the possible error due to the differentiation/filtering procedure is is possible
The three low pass filtering procedures—moving average, Butterworth, and polynomial, applied to differentiation of biomechanical signals have been studied considering both as a simulated reference and experimental kinematic signals
Summary
The biomechanical study of human movement requires a strict integration between experimental data and models to describe motion patterns [1,2]. When dealing with physical parameters that cannot be directly measured, a model-based inverse-dynamics problem has to be solved, which requires the measurement of kinematic quantities, including position, velocity and acceleration of reference points, as well as angular displacement and relative derivatives of body limbs. State-of-the-art measurement systems for kinematic analysis in biomechanics include video or inertial sensors. Experimental signals resulting from the measurements are positions. Experimental signals resulting from the measurements are angles. In both scenarios, some noise affects the measurements, mainly due to electronics and processing of the IMU signals, or to illumination, fast movements, camera resolution and focus in the video scenario [3]
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