Stand for the System of Remote Analysis of Heart Rate Variability

Abstract

The authors proposed structure of the physical well-being monitoring system for working stuff based on heart rate variability (HRV) analysis. To obtain heart rate data, the method of optical plethysmography was used. To filter the signal and form a dynamic threshold, the moving average method is used. To calculate the stress index, the method of statistical processing of the cardiointervalogram was applied. A review of existing developments is carried out and their main disadvantages are identified. Most of the analogical systems uses non power-efficient wireless network technologies such as cellular mobile networks or using user mobile phone as an agent between wearable device and web-server. An algorithm for the analysis of HRV is developed using the Python programming language. The results of the proposed algorithm for fetching cardiac intervals (CI) are compared with the results of the open-source Python HeartPy library. The structure of the mesh network, its main components and principles of operation are described. The proof-of-concept system implementation was launched on development boards EBYTE E73-TBM-01 and photoplethysmography sensor Maxim Integrated MAX30102. Test measurements were carried out and their analysis was performed. To test the system for the possibility of obtaining a quantitative characteristic of the physical condition of the person, test measurements was performed with a duration of 5 minutes on a healthy person aged 22 years old. Also, the raw photoplethysmography data was transferred to a PC to receive the SI using the HeartPy library, and to compare the values obtained. The results show possibility of using HRV analysis in physical well-being monitoring systems. Obtained measurement results show accordance with the state of the test person during the measurements. The vast majority of wearable devices are combined by two common features – Bluetooth module and optical heart rate sensors based on optical plethysmography. These two features of the absolute majority of modern wearable gadgets open the possibility to create systems for monitoring the physiological condition of working personnel with minimal cost. Accurate optical sensors not only measure the heart rate, which does not reflect the actual physiological state of the person, but also open the door to a deeper and more informative analysis of HRV. And having Bluetooth modules above the fourth version, with software changes, allows you to use the mesh network concept, completely changing the point-to-point topology approach and creating many-to-many networks from wearable devices. This enables the creation of reliable energy efficient LANs without a single failure point and the ability to cover large areas. This eliminates the need to use the smartphone as an intermediate link between the wearable device and the server.