A study of sustainable and safe signal processing techniques in wireless body sensor network for heart rate estimation with context awareness

Abstract

Continuous monitoring of vital signs is helpful for the healthcare professionals in early diagnosis of diseases and takes preventive action. Blood pressure, heart rate are some of the vital signs that can be monitored using a wearable device. In order to help the healthcare professional in identifying the situation, context should be recorded. The objective of this research is to design a Body Sensor Network (BSN) to measure Heart Rate (HR) with context awareness sensing. In HR estimation, Motion Artifacts (MA), Least Mean Squares (LMS) algorithm is used. To collect the data, a device is manufactured which can transmit data wirelessly to a database. The selected signal processing methods are applied to these collected data to estimate HR along with the context of the user. Sustainable and Safe Signal Processing Techniques in Wireless Body Sensor Network for Heart Rate Estimation with Context Awareness is developed and performed well.