Cervical Spine Health Monitoring Based On Channel State Information

With the development of the times,more and more people spend a lot of time on mobile phones and computers.This social phenomenon makes the incidence of cervical spondylosis increase year by year.Whether it is a student in school,a white-collar worker in an office,or even a taxi driver,they may maintain an abnormal neck posture for a long time.These people may have symptoms of chronic strain,and chronic strain is the main cause of cervical spondylosis.Accurate detection of neck movement posture has a high effect on the prevention and diagnosis of cervical spondylosis.However,at present,there are few methods for the prevention of cervical spondylosis.The measuring methods of the range of motion of the cervical spine mostly use devices such as goniometers or sensors,and these devices have the problems of difficulty in carrying and inconvenience to use.In summary,it is very necessary to find a new method to monitor neck activity.This method can not only help prevent cervical spondylosis,but also provide data support for doctors’ diagnosis.The purpose of this thesis is to use a method based on channel state information to obtain neck activity signal,so as to research methods of monitoring and identifying different neck activities.The main work of this thesis is as follows:(1)A systematic overview of the current research status of neck movement measurement,and at the same time clarified the principle based on non-contact perception.(2)Constructed a wireless sensing platform.According to the paradigm action of the clinical trial,a commercial wireless network card is used to obtain the wireless signal of the subject’s neck movement,and the amplitude of the wireless signal is extracted as the characteristic information.At the same time,outlier processing,wavelet filtering and other methods are used to complete the preprocessing of the collected original signal data.(3)According to the actual needs of monitoring in this thesis,a one-dimensional convolutional neural network model is proposed.The model is used to process and analyze the different experimental data in this article,and the performance of the model is verified by comparison,which can better identify different neck activities.(4)The neck movement experiment was used to measure the different postures of common neck activities,and the neck movement experiment was used to identify the neck movement at different degrees of curvature,so that people can be reminded of abnormal neck movements in time according to medical guidance.A variety of comparative experiments have proved the effectiveness of the model proposed in this thesis in solving the problems studied in this thesis.At the same time,according to different experiments,the optimal number of wavelet decomposition layers is determined to complete the high-precision recognition of neck activities.To sum up,the neck activity detection system proposed in this thesis can effectively obtain people’s normal neck activity information and can objectively and accurately identify different neck activities,which is beneficial to people to maintain the health of the cervical spine,and also for the neck.It provides a new non-contact method for neck activity monitoring.