Study On Key Techniques Of The Smart Clothing Based Elderly Health Monitoring System

Our society is aging.An aging population not only brings health care,economic,and political issues to the society,but also brings pressure to the family.In daily life,in addition to the needs of public facilities and medical resources,the elderly also has the need for daily family care.Daily family care can effectively avoid the sudden situation of the elderly,but the caregiver needs to take care of the elderly at all times.The tele-monitoring system can enable caregivers and doctors to keep learning about the elders' health status,and to detect the occurrence of abnormalities in a timely manner.To a certain extent,the pressure caused by the daily care of the elderly can be mitigated.In this thesis,on the basis of a wearable smart clothing health monitoring system,the design and implementation of gait asymmetry detection,sleep monitoring,and motion monitoring based on human body gravity acceleration were introduced,and a mobile phone APP for the health monitoring system were designed and implemented.The main research contents of this thesis include:1.A gait asymmetry detection method based on gravity acceleration was implemented.The body's vertical acceleration of the waist was collected,and the frequency-domain quadratic integration method was used to calculate the trajectory of the human center of gravity displacement.The gait feature parameters were extracted through the displacement trajectory,and the gait asymmetry coefficient was calculated to evaluate whether there was asymmetry in the gait.2.A sleep quality monitoring method based on the gravity acceleration was realized.The human body chest triaxial gravity acceleration signal was collected to recognize the different characteristics of the triaxial acceleration signal under different human body conditions and to realize the turning-over detection.By calculating the number of turning-over per minute,a linear statistical model was used to distinguish between awake and sleep states.The statistics on the status of sleeping time,waking up time,deep sleep time,and light sleep time,etc.were realized to achieve sleep monitoring.3.Methods for activity monitoring based on gravity acceleration were realized.Activity monitoring included functions of step count,mileage calculation,and physical energy consumption calculation.Human body chest triaxial acceleration signals were acquired.The combined acceleration was calculated.The peak detection method and the dynamic threshold method were combined to achieve the step counting function.Using the step-by-step method based on acceleration,the step length was predicted and the mileage calculation was realized.Using the physical energy consumption estimation method based on human body parameters and exercise parameters,the calculation of sports physical energy consumption was realized.4.A smart phone APP for elderly health monitoring system was designed and developed.The functions of the smart phone APP mainly included real-time positioning,real-time ECG,historic ECG playback,sleep monitoring,activity monitoring,fall detection,and gait analysis.When the software was used,the user was required to register and log in.The human ECG data and three-axis acceleration data were collected through the Bluetooth connection smart clothing,and the monitoring information was displayed,stored,and uploaded on the mobile phone through corresponding calculations to realize an abnormal alarm.The doctor could always know the status of the elderly by accessing the server,and the diagnosis information and suggestion was pushed to the mobile phone APP.