A Scalable BLE-Based Signal Acquisition System for Wearable Devices

The lack of healthcare equipment in rural areas in the Philippines motivated the exploration of wearable devices in measuring vital physiological signals like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). OpenBCI is an open-source hardware and software platform for biopotential acquisition that is less expensive and more compact than traditional equipment. However, the current software only supports one device at a time. Hence, the aim of the study was to develop a platform that can simultaneously acquire biosignals from multiple wearable peripherals by taking advantage of their Bluetooth Low Energy (BLE) capability. BrainFlow is an open-source software that provides a uniform data acquisition API for OpenBCI devices and can support new devices by integrating in its backend. Signals were acquired simultaneously from two OpenBCI Ganglion boards and a Thalmic Labs Myo armband, through concurrent threads. Feature extraction was also implemented using the NeuroKit2 Python library for data analysis. The functionality of the proposed system was achieved; data were synchronously logged from all devices. The feature extraction component was able to calculate the heart rate and PQRST peaks for ECG; the mean absolute value, wavelength, root mean square, maximum fractal length, and average power for EMG; and brainwave decomposition for EEG.