An IoT-Based Telemedicine System for the Rural People of Bangladesh

IoT devices can enable low cost and interactive health care services. In this paper we have proposed an affordable telemedicine system to bring healthcare services within the reach of the rural people of Bangladesh. Proposed system enables transmission of patient’s body parameters in real-time to a remote doctor. The proposed system also has real-time patient monitoring capability which is based on ECG signal classification. A feed-forward neural network is used for ECG signal classification on an embedded ARM processor. For low power operation, we have utilized fixed-point (integer) arithmetic instead of floating-point arithmetic for the ECG signal classification task. Proposed fixed-point implementation is 1.06x faster than floating-point implementation and requires 50% less memory to store the neural network model parameters without loss in the classification accuracy.