A Study Of Running Knee Injury Risk Detection Technology Based On Bipedal Pressure Sensor And Gyroscope

With exercise becoming a major trend in our lives,many smart wearable products have emerged to monitor the health of human movement.The function of popular wearable devices on the market is mainly to detect the number of steps,routes and calories consumed when people walk to give feedback to users on their sports data information.However,for people who require more information about their exercise data,it is difficult for existing devices to meet these needs.For those who need more detailed exercise information,such as runners who love long-distance running sports,there is always an unscientific gait when running for a long time,which can damage knees and foot ligaments if the posture is not corrected.Therefore,it is very important to scientifically install sensors on the feet to detect motion parameters in real time and provide useful suggestions and information about the user’s running posture and knee injury risk situation.Based on the background of assessing the risk of knee injury during the runner’s exercise,there is a need to detect the foot pronation,stride frequency and stride amplitude that occurs during the running process.The existing gait detection devices have great limitations,many portable devices have a single function and do not have the function of measuring multiple parameters at the same time,while the feature-rich devices are expensive and poorly portable,to address these problems this project designed a gait information acquisition system based on pressure sensors and gyroscopes.The system collects the pressure signal of the foot and the acceleration signal of the foot during the running process through the reasonable layout of the pressure sensor and gyroscope,and then can effectively detect the running parameters of the human body through the research and analysis of the collected signals.Firstly,this project designs a gait parameter acquisition system with STM32 microcontroller as the microprocessor and foot pressure insole and gyroscope as the signal acquisition module;secondly,the experimental data are collected by designing experiments,and for the missing problems of the collected data,the interpolation method is used to ensure the integrity of the data,so as to pave the way for the subsequent gait analysis;again,in order to complete the detection of three parameters that are related to the risk of knee injury during running,this project completes the detection of foot rotation front and the calculation of stride frequency based on fuzzy inference algorithm.Secondly,in order to complete the detection of the three parameters that are highly correlated with the risk of knee injury during running,this project completed the detection of foot rotation front and the calculation of step frequency based on fuzzy inference algorithm,and completed the calculation of step width using zero speed update technology based on the quadratic number theory;finally,based on the acquired motion parameters and objective parameters,the running knee injury risk assessment was completed based on logistic regression algorithm.