Study On Human Motion Recogniton Based On Pressure Sensing Gait

In information network era, biological feature recognition technology is rapidly developed as an emerging advancing front technology. People begin their new life and practice when they gradually realize the chang of cognition and behavior way. On the basis of information network, human inherent physiological property(fingerprint, face image, ins, etc.) and behavior characteristics(handwriting, voice, gait, etc.) advance the development of biological feature recognition theoretic study and technology according to technologies such as computing system and optics, acoustics, biosensor and biostatistics, in the recognition of person and human motion behavior.As one of the biological feature recognition technologies, gait recognition aims to find and extract variation between individuals from human motion behaviors to achieve automatic behavior identification. Because gait recognition technology has the characteristics of low requirement on the system resolution, data collection equipment satisfying people's habits, non-invasive and difficult to hide, a large number of scholars aborad and at home have been deeply attracted to study on it. Gait recognition technology is gradually playing its role in intelligent monitoring, clinical, rehabilitation, movement analysis, design of intelligent artificial legs, and many other areas of identity, it has become the second-generation biometric identification technology representatives.This paper study on human motion recognition from three aspects: the collection, token and recognition of gait based on the fundamental theory of pattern recognition, statistic learning theory, signal processing, etc. On the basis of establishing the gait recognition system, it focus on the research of gait recognition algorithm based on physiological parameter feature extraction, fall recognition algorithm based on plantar pressure sensing and human motion recognition algorithm based on support vector machine. A mase of experiments have been done on gait recognition system to validate the validity of the algorithms.The main research findings include the following four areas:â‘ The gait recognition system has been studied. The hardware of the system should not change or influence an individual's weight balance and walking habits. Also, it should be flexible to meet different individuals'needs, be convenient to wear and be capable of data gathering and transmitting in real time. This paper researchs on the insole-based plantar pressure measuring method. The sensors is located on the positions that has to be measured. Because of the closely touch between insole and the feet, the parameters such as plantar pressure and time can be determined continuously and monitored and fed back in real time. The size of insole is adjustable,thus can be used in various kinds of shoes. The design of the insole pressure sensing module, the microcomputer-based data gathering module, the gait data transmission module and the background data processing module are also studied. At the meanwhile, the software and hardware is developed to establish a prototype system.â‘¡Gait is the motion gesture of human body during the motion process. The way to gain the information from the physiological parameters in motion to recognize the gait is the key of the study. The method to extract physiological parameter feature can solve the mass data and high dimensions problems of the input of plantar pressure measurement. This paper adopts pressure sensor to apperceive human motion, and gains human plantar pressure by contact data gathering. After the experiments and algorithm analysis, the eigenvalue of physiological parameter correlative to gait is defined. Then, the gait recognition algorithm based on physiological parameter feature extraction is proposed.â‘¢Fall is a typical gesture of human actions. The fall of elders is a dangerous action. This paper describles all the pressure change characteristics related to fall and studies on the gait feature matrix by the pressure sensing of fall events. The plantar pressure related gait feature during falling is defined and extracted. Binary support vector machine model is established and the fall recognition algorithm based on plantar pressure sensing is proposed. The experiments proved that it has high reliability and veracity on fall detection and recognition.â‘£This paper studies on a new recognition method to apperceive human motions such as walking, running and jumpping by plantar pressure. It extracts gait feature from the initial pressure data of those motion, defines motion feature, establishs eigenvector matrix, describes and distinguishs the pressure change characteristics of motions. The optimization problem of SVM model is analyzed based on multi-classification SVM and genetic algorithm. The human motion recognition algorithm based on support vector machine is proposed. According to the validating of the experiment system, the reliability and veracity on motion recognition is proved. This method can be used to measure the amount of exercise.To sum up, the algorithms proposed in this paper are a new exploration in human motion recognition fields. Information network will advance the development of the biological feature recognition technology. Emerging multi-mode developing trend makes us a further reseach on human motion recognition by pressure sensing gait.