Research And Application Of Human Activity Analysis Based On Motion Perception

With the development of internet of things and various kinds of sensor technology,human activity perception recognition technology based on artificial intelligence is widely used in many fields,such as health care,intelligent pension and intelligent security.More and more applications have been made to realize daily health monitoring and disease early warning by collecting sensor data for human activity perception and recognition.Parkinson's disease,as a neurodegenerative disease that troubles many middle-aged and elderly people,has become a target of many applied research.Current research focuses on the diagnosis of Parkinson's disease and rehabilitation training.The relevant data analysis is mostly based on the original sensor data,and the accuracy is poor and the efficiency is low.First,we use U-Gait electronic walkway system to collect gait features of Parkinson's disease patients and normal persons and study how to use these gait features for Parkinson's disease detection.Then we use human activities recognition method based on wearable sensor data to monitor and evaluate daily activities of Parkinson's patients during motor rehabilitation.The main research work of this dissertation includes the following aspects:(1)The original plantar pressure data of Parkinson's disease patients and normal persons is extracted from U-Gait electronic walkway based on flexible array pressure sensor.Detrended fluctuation analysis(DFA)and detrended cross-correlation analysis(DCCA)are used to validate the validity of plantar pressure data for disease diagnosis.The experiment results show that the autocorrelation and cross-correlation of plantar pressure time series in normal persons are both higher than those in Parkinson's disease patients,which is consistent with medical research results.Then,we design a method to calculate 18 gait spatio-temporal characteristics through footprint reduction,track generation,denoising,expansion optimization and segmentation processing.In order to reflect the gait characteristics of Parkinson's patients with turning difficulty,the parameters of turning gait at the two corners of U-Gait are also extracted.We preprocess the gait data by eliminating individual height difference and normalization.In addition,the T-test result certifies the significant differences of the gait features between Parkinson's disease patients and normal persons.And the reliability of gait features is verified by ICC method.The results provide a feasible and effective basis for the subsequent use of gait features to establish machine learning model for the identification of Parkinson's disease.(2)Aiming at the negative effect of redundancy or irrelevance between gait features on the performance of Parkinson's disease recognition model,this paper presents a new feature selection algorithm-IWFS,which combines Filter method with Wrapper method.First,IWFS algorithm uses ReliefF method to arrange the original features set according to the descending order of correlation.Then it uses Wrapper method to incrementally select the features which can improve the classification accuracy.The experimental results validate the good classification performance of this model,and provide a strong basis for the practical diagnosis of Parkinson's disease.(3)To meet the needs of daily activities monitoring and evaluation in the rehabilitation process of Parkinson's patients,this paper realizes the recognition of human daily activities through the data of triaxial accelerometer and gyroscope sensors.Due to the diversity and complexity of human activities,activity recognition methods often can not obtain good performance.Planar classifiers are mainly used in current research and only one classifier is constructed to identify all predefined activity sets.The hierarchy between human activity categories is ignored.And the research on feature using mainly focuses on extracting a large number of various time and frequency domain features.In fact,there may be some redundancy features and irrelevant features affecting the performance of the activity recognition model.We proposed a new human activity recognition model based on hierarchical structure and class-dependent feature selection.This model transforms the multi-class activity recognition problem into a multi-level activity recognition problem.And the feature selection method based on class-dependent selects an optimal feature subset for each activity class.The feature subsets of different classes may be different.Experimental results show that the proposed human activity recognition method has better recognition performance and generalization ability than other methods.