Gait phase recognition method based on inertial sensor

Abstract

The invention relates to a gait phase recognition method based on an inertial sensor. The gait phase recognition method based on the inertial sensor is characterized in that extreme value judgment iscarried out by utilizing body side angular velocity collected by the inertial sensor bound at an ankle. A complete gait cycle can be roughly divided into two phases of a swinging state and a supporting state, the supporting state can be divided into four stages of heel landing, full sole landing, heel off and forefoot off in sequence, when a foot is in the full sole landing stage in the supportingstate, the foot is static relative to the ground, that is, the body side angular velocity at the ankle is close to zero, by utilizing the characteristic that the angular velocity approaches zero whena foot touches the ground, compared with a pressure insole calibration result, it can be known that the whole supporting state phase can be recognized by detecting two maximum values of the input angular velocity, and the aims of high accuracy, low time delay, low hardware requirement and easy use can be achieved.

Description

technical field [0001] The invention relates to a gait phase recognition method based on an inertial sensor, and belongs to the technical field of gait phase recognition. Background technique [0002] The exoskeleton robot is a human-machine system, and various information needs to be exchanged between the human body movement and the exoskeleton movement. The exoskeleton needs to recognize the gait of the human body and the movement intention of the human body to drive the movement of the exoskeleton. Gait information needs to be analyzed through the gait data collected by the sensor system. The gait data collected by different sensor systems are also different, so the gait characteristics are also different, so the methods of gait recognition are also different. The main gait data include: joint angle data, acceleration data of the limbs, myoelectric signals of relevant muscle groups, brain wave signals, plantar pressure signals, and video-based image data, etc. [0003]...

AI Technical Summary

Problems solved by technology

[0005] (1) Gait phase recognition technologies based on pressure insoles, light-capturing camera equipment, electromechanical or EEG acquisition equipment have high requirements for hardware. These equipment are not convenient for users to wear quickly and some of them are relatively expensive

[0006] (2) Problems with machine learning gait phase recognition algorithms based on inertial sensors: machine learning algorithms require a large amount of training data to improve performance, so it takes a certain amount of time to collect training sets for the algorithm, which cannot be done for users with large differences Ready to wear and use; the generalization ability of machine learning algorithms varies, and the adaptability to different terrains depends on the quality of the training set; it is difficult to control the delay of machine learning algorithms at the millisecond level, and there is a sense of lag in actual use

[0007] (3) Problems with the threshold discrimination gait phase recognition algorithm based on inertial sensors: the threshold of the threshold discrimination algorithm is difficult to define, and a large amount of data is required to provide a basis; the threshold discrimination algorithm continuously adjusts parameters according to different users and terrain needs, and is robust Poor performance; Threshold discrimination algorithms are prone to misjudgments and missed judgments for special actions in the gait cycle

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