Real-Time Processing and Analysis for Activity Classification to Enhance Wearable Wireless ECG

Health care facilities in rural India are in a state of utter indigence. Over three-fifths of those who live in rural areas have to travel more than five km to reach a hospital and health care services are becoming out of reach for economically challenged communities in India. Since rural communities experience about 22.9 % of deaths due to heart disease [1], there is a need to improve remote ECG monitoring devices to cater to the needs of rural India. The existing wearable ECG devices experience several issues in accurately detecting the type of heart disease someone has due to the presence of motion artifacts. Hence, even though wearable devices are finding their place in today's health care systems, the above-mentioned issues discourage a doctor in depending upon it. So to enhance the existing wearable ECG device, we designed a context aware system to collect the body movement activity (BMA). In this paper, an innovative BMA classifier has been designed to classify the physical activities of user from the real-time data received from a context aware device. The test results of the BMA classifier integrated with the complete system show that the algorithm developed in this work is capable of classifying the user activity such as walking, jogging, sitting, standing, climbing upstairs, coming downstairs, and lying down, with an accuracy of 96.66 %.