Research On Key Technologies Of Human Body Communication On Auxiliary Medical Diagnosis

Human body communication technology is a short-distance information transmission method that uses the human body as information transmission.Compared with traditional wireless communication technologies such as Bluetooth and infrared,human body communication technology has the advantages of simple structure,high confidentiality,low power consumption,less susceptibility to external noise interference,and lower human body damage.Using human body communication technology to collect and transmit body index information through portable medical sensors for remote real-time auxiliary medical diagnosis is not only an important means of disease prevention and health protection in intelligent medicine,but also a research hotspot in the future.However,the current research on the medical auxiliary diagnosis using human communication technology still has some problems: First,due to material limitations,the acquisition of human physiological signals by micro-sensing is not accurate.In addition,there is noise interference in the process of collecting human physiological signals through microsensors.This problem leads to problems such as low acquisition accuracy and long acquisition time in the process of human physiological signal acquisition,which cannot meet the needs of actual medical applications.Second,the existing human body physiological signal transmission channel modeling and analysis methods only use the external shape of the human body to perform simple model and analyze,and do not consider the difference of the electromagnetic characteristics of each layer of the human body.Therefore,the existing analysis methods fail to accurately reflect the influence of the changes in electromagnetic characteristics in different tissue layers of the human body on signal transmission.Third,the human physiological signals extracted from medical auxiliary diagnosis contain a large amount of non-origin information,and there is a lack of clear feature extraction and analysis methods for human physiological signals in practical application,and the human physiological signals often exhibit waveform drift.which leads to the difficulty of large-scale promotion of the current methods through using human body communication for auxiliary medical treatment.In order to solve the above problems,this paper has conducted an in-depth study on the acquisition,processing,transmission and application in auxiliary medical treatment of human body signals from both theoretical and application aspects.It mainly includes the following three aspects:First,in response to the inaccurate acquisition of human physiological signals by microsensors,this paper proposes a new design method for signal sensors,and designs a signal sensor using porous silicon as a microcavity.Through redesigning microcavity structure and selecting appropriate masks,the sensor designed in this paper can form a beam membrane structure in a specific area,and use the single crystal characteristics and porous morphology of porous silicon to achieve accurate acquisition of human physiological signals.In addition,in view of the low accuracy and long acquisition time of the existing methods for collecting human physiological signals,this paper proposes the method of integrated amplifier circuits to improve the conversion accuracy of discrete samples,which by adding AD conversion control unit,digital filter converters to improve the accuracy of the analog-to-digital converter,in order to achieve the purpose of improving the acquisition accuracy and acquisition time of human physiological signals.The simulation experiment results show that the acquisition accuracy of human physiological sensor signals using the method in this paper is more than 99%,and the network time,connection time and acquisition time are less than 50 ms,which are greatly improved compared with traditional algorithms.Second,in view of the existing analysis methods that fail to accurately reflect the effects of changes in the electromagnetic characteristics of different tissue layers of the human body on signal transmission,this paper proposes an equivalent circuit model analysis method for human body communication technology of wearable devices.The characteristics of the tissue layer of the human body are analyzed to obtain the channel gain of the whole human body communication.The human body communication model is established using the finite element simulation software Ansys HFSS to better simulate the human body communication model.The simulation experiment shows that the result of the simulation model analysis and the calculation result of the equivalent circuit model analysis method are less than 1 d B,which verifies the effectiveness and accuracy of the equivalent circuit model analysis method.Third,in the current algorithm,the non-intrinsic information of the human physiological signal is redundant,the waveform of the human physiological signal drifts,and the signal sample feature is difficult to extract.This paper proposes to use the wavelet transform method to filter out the non-intrinsic information of the human physiological signal,adopt the feature extraction method based on swiping window to extracts the characteristics of human physiological signals to avoid the influence caused by the waveform drift of human physiological signals,and utilize the iterative self-organizing data analysis method to classify and identify the extracted features of human physiological signals.The method proposed in this article solves the problems in human body signal acquisition,transmission and analysis,which achieves the purpose of improving the quality of medical auxiliary diagnosis.The experimental results show that the method can improve the effectiveness of medical auxiliary diagnosis more than traditional methods.In summary,this subject first designs a new type of sensor from the perspective of information collection,optimizes the accuracy of micro-sensors in acquiring human physiological signals,and proposes and establishes a reasonable method for accurate acquisition of sensor signals and denoising amplification.Secondly,a wearable device-based human equivalent circuit analysis method is proposed to better simulate the human body communication transmission model for stabilizing the transmission quality of human body communication.Finally,using this method to diagnose 80 experimental subjects,medicalassisted diagnosis accuracy is higher than the maximum like-like method,semantic knowledge base method,the experimental results effectively verify that this method has a higher effectiveness of medically assisted diagnosis,can be used as an important auxiliary method of medical diagnosis of human health.In addition,the diagnostic sensitivity of the comparison of the three methods has been significantly improved,can effectively distinguish the experimental subjects when abnormal symptoms of the human physiological signals,through the collected human physiological signals to achieve the accurate diagnosis of abnormalities,can be used as a medically assisted diagnostic means in practical applications.