Reconfigurable Architecture of Neuro-Physiological Sensors for Mobile Health System

Abstract

Reconfigurability in a Body Sensor Network (BSN) increases the scalability and heterogeneous potential of the sensor network to provide efficient patient-specific monitoring. Therefore, in this work, a key question is addressed: how to design a BSN with inherited modularity and scalability? To answer this question, we have designed integrated wearable Smart Sensor Nodes (SSN) consisting of EEG and piezo-resistive sensors to measure brain and heart-rate signals, respectively, at real-life settings. The modularity in EEG sensing is introduced by using a novel analog front end that can measure brain signals without using the conventional Driven-Right-Leg (DRL) circuit. The reconfigurability in the network is realized by connecting SSNs to a Command Control Node (CCN) using a five-pin digital Inter-Integrated Circuit (I2C) bus interface at 100 kbps bus-speed. The CCN synchronizes the attached SSNs every second, aggregates data from the SSNs and wirelessly sends the data via a Bluetooth transceiver at a baud rate of 115.2 kbps. The network is scalable to any SSN attached with or detached from the bus. This allows reconfigurability and hardware node upgrade without the redesign of the entire system. We have functionally validated few custom-designed SSNs (three EEG SSNs and one heart rate variability SSN) against the commercially available EEG and pulse oximeter. The proposed reconfigurable architecture promises a scalable BSN in mobile health (mHealth) that can be connected to any neuro-physiological sensor for data acquisition in the practical settings.