Research On Motion Measurement And Recognition Method Of Human Arm Based On MEMS Sensors And Zigbee Networks

Human motion capture and detection technology is widely used in film and television creation,video games,action analysis,sports research and training,rehabilitation,virtual reality,human-computer interaction,robot autonomy control and other fields.MEMS inertial sensor is a kind of Micro-Electro Mechanical Systems(MEMS)sensors,with miniaturization,low power consumption,low cost and anti-interference ability.The emergence and development of MEMS inertial sensors have promoted the development of human motion capture and detection methods based on MEMS wearable inertial sensors.For human body motion capture and detection method based on MEMS sensors,the MEMS inertial sensor are used to measure the acceleration and angular velocity information of human motion,and then the attitude angles of human motion are calculated.Further the pattern recognition method is used to identify the human body movements.Compared with other methods of human motion capture and detection,it has the advantages of simplicity,low cost,easy access to wear and nice performance of real-time.However,due to the large drift errors of MEMS inertial sensors,the accuracy of attitude angles is not often high,which further leads to a low recognition rate.Therefore,this paper carried out the research work of human body motion capture and detection method based on MEMS wearable inertial sensors,and aimed at the measuring and recognizing of arm movements.It focuses on a high accurate attitude angles calculation and an effective feature extraction of pattern recognition,to achieve a more accurate detection and identification of human body movements.First of all,the paper determined the system structure scheme based on Zigbee wireless transmission.On this basis,the overall design of the system scheme was established.The whole system consists of the motion detection module based on MEMS sensors,the module of Zigbee wireless network for data communication and the module of host computer motion identification algorithm.The module of motion detection based on MEMS sensors was designed to measure the angular velocity,acceleration and magnetic field information when arm moving.The Zigbee wireless network then uploaded the measured data to the PC.The attitude angles were calculated and identified based on the Labview platform on the PC.The measurement and calculation of attitude angles is one of the key of this method,and was deeply analyzed and researched in the paper.In respect of the attitudes calculation,the advantages and disadvantages of inertial system and the attitude reference system are compared and analyzed.In order to improve the accuracy of attitudes estimation,the paper used the Kalman filter data fusion method to combine the inertial system and the attitude reference system together effectively.And the value of the noise variance of the Kalman filter was adjusted according to the measured acceleration,which could weaken the influence of the motion acceleration on the attitude angles estimation,and then improve the accuracy of the attitudes estimation.Both the simulation analysis and the experimental study showed that the proposed Kalman filter algorithm with the capability to predict and adjust the observed noise variance could realize more accurate attitudes estimation than that with the traditional methods.On this basis,the paper further analyzed the arm movement model,and established the hand movement trajectory model.The four kinds of typical arm movement models were analyzed,and the trajectories of the wrist were obtained.The corresponding experiment verified the calculation model of arm movement.The paper also established the error model of trajectory calculation,and analyzed the influence of attitudes angle on the accuracy of trajectory calculation.The identification of the state of movement is another key issue to be addressed.The traditional method is to extract the time domain characteristics of the signal measured from sensor as the input of the pattern recognition algorithm.The time domain feature is vulnerable to the human body and the environment,resulting in the difference between the eigenvalues unobvious,and thus makes the recognition rate of pattern recognition is not high.The features extracted from the hand trajectories were combined with the time domain features extracted from the measurement signals so that the recognition rates of the BP neural network and the SVM algorithm for four types of typical arm movements are increased and reach up to 97.14%and 100%,respectively.Based on the preceding researches,a wireless transmission test system based on Zigbee technique was established.The corresponding Labview software was designed,which has the functions of receiving,displaying and saving of data,calculation of attitude angles,trajectories feature extraction,and identification of BP neural network.And the whole system functions were tested.The test results showed that wireless transmission based on Zigbee network technique was normal,and the host computer programs for data detection and processing were running stably,and realized the identification of four kinds of typical arm movements correctly.