Gait phase analysis method for segmented local peak detection

Abstract

The invention provides a gait phase analysis method for segmented local peak detection, and relates to the field of gait analysis, in particular to a gait phase analysis method related to the foot angular velocity. The gait phase analysis method for segmented local peak detection is disclosed to solve the problem that a double-peak detection method used for gait phase division according to inertial data is poor in self-adaptability. The method is technically characterized by comprising the two steps that pitch angular velocity data of a foot in a gait movement is obtained; according to movement characteristics of a swing stage and a landing stage in the gait movement, the non-forward swing angular velocity is segmented by extracting the forward swing angular velocity. Local extreme valuesare extracted to replace a traditional peak detection method, and extraction of information of two key points including a heel contact point and a toe-off point during the gait movement is achieved. The method can be applied to the field of gait analysis.

Description

[0001] 1. Technical field [0002] The invention relates to the field of gait analysis, in particular to a gait phase analysis method based on the angular velocity of feet. [0003] 2. Background technology [0004] Gait phase analysis is a branch of the field of gait analysis, which has a wide range of applications in medical rehabilitation diagnosis, athlete training evaluation, and human-computer interaction. With the increasing development of MEMS technology, wearable devices based on MEMS devices are gradually popularized. By wearing MEMS devices, you can obtain inertial data such as acceleration and angular velocity of the wearing part in gait movement. This method is low in cost, high in accuracy, and is subject to The limitation of test environment and test time is small, and it has broad application prospects in the field of gait analysis. [0005] The inertial data commonly used in gait analysis are three-axis angular velocity and three-axis acceleration, while gait ...

AI Technical Summary

Problems solved by technology

In view of the fact that the pitch angular velocity of the foot has good periodic characteristics when the human body is walking, at present, the single-axis angular velocity is often selected for bimodal detection to achieve key point information extraction, and bimodal detection often uses the horizontal time window length threshold and longitudinal peak threshold detection As a judgment criterion, the adaptability of the scheme to different test individuals and different test situations is poor. When the scheme of dynamically updating the threshold is introduced, the adaptability of the scheme to individuals is enhanced, but the adaptability to different test situations is still poor. Such as the gait initialization stage, gait stagnation stage, etc., the update of the threshold value causes the threshold value to deviate from the ideal value. Before the threshold value is updated to the ideal value, reasonable measurement data will often get wrong detection results due to the threshold value deviating from the ideal value.

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