Research On Wearable Blood Oxygen Sensor Monitoring System And Its Application In IoT Health

As an important physiological parameter of human health,real-time and long-term Sp02 monitoring has a great significance for human health.Because the traditional oximeter is worn on the wrist or at the tip of the finger,there are problems like large volume,uncomfortable to dress.And currently,mobile medical wearable devices on the market use single sensor data to communicate with mobile phones and Personal Computer(PC),lacking of a wide range of mobile connection design concepts for wearable device.With the development of Internet of Things(loT)technology,it has become a new trend to connect the monitoring of physiological parameters to mobile Internet of Things(loT).Given of this technology,this article has done the following research works:1.Developed a wearable SpO2 monitoring system with small size,low power consumption,and access to mobile medical loT.The design of SpO2 monitoring system based on medical loT mainly includes hardware system construction,system software design and algorithm design.Considering low power consumption and miniaturized design,This paper choose the BLE 4.0 chip CC2541,heart rate oximetry chip Max30102 as the hardware foundation,and built blood oxygen collection front end and mobile relay system based on reflective photoelectric detection.The system software design front-end and relay are developed based on IAR and Keil compiler respectively,which realizes the front-end Bluetooth and relay,relay and cloud data communication and data interaction,mobile access function.This system can realize real-time data display,storage,historical data query and other functions.As a result,fulfilled needs of SpO2 remote,real-time,long-term monitoring.2.Using Mathematical Morphology-Double Density Wavelet Transform(MM-DDWT)combined filtering algorithm for pulse wave denoising processing.Comparing traditional wavelet transform threshold denoising and double tree complex wavelet transform threshold denoising results,This paper innovatively uses the mathematical morphology to remove the baseline drift of the signal and then uses dual-density wavelet transform threshold denoising to remove noise interference.This algorithm can filter the high frequency noise signal in the pulse wave more effectively,as well as avoiding the loss of effective signal in the process of denoising by traditional wavelet transform,and better retain the characteristic information of the pulse wave.The system also integrates a three-dimensional acceleration sensor system,and determines the movement state of the human body by experimentally setting the acceleration threshold to classify the blood oxygen signals under different motion postures.The system designed in this study is light,portable and simple to use,and provides an idea for the design of the blood oxygen saturation monitoring system based on the medical Internet of Things.