Research And Application Of Human Behavior Recognition Method Based On Physiological Signals

In recent years,with the rapid development of sensor technology and smart devices,human behavior recognition has become an important research hotspot,which has important research significance and wide application prospects in medical monitoring,intelligent security,elderly care and indoor positioning.In the field of human behavior recognition,the method based on the sensor signal of the wearable device has the advantages of good portability,low power consumption,anti-environmental interference,etc.,and is more suitable for practical application scenarios.With the deepening of research,the current human behavior recognition research based on sensor signals can achieve higher results on public data sets,but the public data sets are collected in the ideal environment of the laboratory and the accuracy calculation is performed on the server.The actual application scenarios of wearable devices are more complicated,and there are problems such as irregular movement of wearable users,differences in speed,and differences between users.To solve these problems,this thesis designs a deep network model of human behavior recognition based on the residual network for the lightweight computing platform of smart wearable devices,and combines Hypergraph Learning and Transfer Learning to improve the accuracy of user behavior recognition in actual application scenarios.The main work of the thesis is as follows:1.In order to solve the problem of low accuracy due to non-standard user actions and speed differences in actual wearable scenes,this thesis designs a behavior recognition model based on a ResNet based on a lightweight wearable computing platform.The experimental results show that,under the USC-HAD public data set,it is2.32% higher than the existing literature,reaching an accuracy of 92.72%.It achieves a higher accuracy of 94.8% under the 9 categories of raw data of couriers.2.In actual application scenarios,differences in user's personality factors(height,weight)have an impact on the accuracy of behavior recognition.In order to improve the universality of the model,this thesis proposes to integrate personality factors with sensor data and use super Hypergraph Learning method for behavior recognition.The experimental results show that,under the USC-HAD public data set,it is 3.1% higher than the existing literature and achieves an accuracy of 93.3%.Based on the60-dimensional features extracted by the courier's data,the personality factor isincreased by 3.95% to achieve 90.5% accuracy.3.In order to improve the generalization performance of the model,the semi-supervised cross-user behavior recognition model was first designed for the problem of low accuracy in testing new user data sets due to user differences in actual scenarios.The experimental results show that in express under the cross-user data set,the recognition accuracy increased from 74.47% to 94.38%.Secondly,this thesis proposes the unsupervised domain adaptation method CORAL based on cosine weighting for cross-user human behavior recognition.The experimental results show that the recognition accuracy of the courier data set across users increases by 5.09% to86.77%.On the WARD public data set,the method proposed in this thesis is improved by 6.65% compared to the existing literature,and achieves 90.94% accuracy of behavior recognition.On this basis,the human behavior recognition model designed in this thesis is applied to the actual application of calculating the labor volume of couriers,a model for calculating the labor volume of couriers is designed based on the Analytic Hierarchy Process,and a system for calculating the labor volume of couriers is designed and implemented.The system's intelligent working mode helps reduce the rate of courier turnover.