Research And Implementation Of Gait Evaluation Technology Based On Pressure Sensor And Inertial Measurement Unit

Scientific sports can improve the health of the human body.Quantitative analysis of sports activities in people's daily life can effectively avoid injuries caused by improper exercise and achieve the best effect of exercise for promoting health.Walking is the most important exercise mode in people's daily life,and gait data has very important research value for evaluating human health.With the gradual advancement of 5G commercial use,the rapid development of the Internet of Things(IoT)has led people to explore the Internet of Bodies(IOB).Gait evaluation technology based on wearable gait system has become the research focus of today's scholars.This thesis adopts the idea of multi-sensor data fusion,uses pressure sensors and inertial sensors to collect dynamics and kinematics data during human walking,analyzes and calculates through related algorithms,and gives an objective and quantifiable gait assessment.The main research content of the thesis is as follows:First of all,accurate gait phase recognition and gait cycle segmentation are the basis for the analysis of individual gait.This thesis proposes a human gait modeling method based on Ground Reaction Forces(GRFs)signals,which makes full use of the GRF signals obtained from 8-channel pressure sensors to realize smooth and continuous gait phase recognition through fuzzy logic inference;Taking into account the internal differences of individual gait,the phase sequence of the individual gait is not preset,and the obtained gait phase sequence is used for gait cycle segmentation to obtain a specific model for each person's gait.Then,since the GRF signal can only give an estimate of part of the time metric,the introduction of inertial sensors provides an index for gait assessment technology that can fully summarize individual gait performance.In order to solve the error problem in spatial measurement,this thesis introduces an error correction algorithm based on quaternion to accurately estimate the foot direction by fusing the static low frequency information provided by the acceleration sensor and the dynamic high frequency information provided by the angular velocity sensor.At the same time,the Zero Velocity Update(ZVU)algorithm is introduced to periodically correct the velocity,alleviate the increase in uncertainty caused by the acceleration error integral,and further improve the estimation accuracy of the gait space measurement.Finally,the proposed algorithm is verified by experiments.The experimental results show that the proposed human gait modeling method based on GRF signals can effectively distinguish and quantify various patterns of gait;After the introduction of heterogeneous sensor data fusion technology,it can effectively compensate for the shortcomings of a single sensor and improve the estimation accuracy of gait time-space metrics,which fully verifies the feasibility and effectiveness of the gait evaluation technology proposed in this thesis.