Digitally controlled feedback for DC offset cancellation in a wearable multichannel EMG platform

Abstract

Wearable systems capable to capture vital signs allow the development of advanced medical applications. One notable example is the use of surface electromyography (EMG) to gather muscle activation potentials, in principle an easy input for prosthesis control. However, the acquisition of such signals is affected by high variability and ground loop problems. Moreover, the input impedance influenced in time by motion and perspiration determines an offset, which can be orders of magnitude higher than the signal of interest. We propose a wearable device equipped with a digitally controlled Analog Front End (AFE) for biopotentials acquisition with zero-offset. The proposed AFE solution has an internal Digital to Analog Converter (DAC) used to adjust independently the reference of each channel removing any DC offset. The analog integrated circuit is coupled with a microcontroller, which periodically estimates the offset and implements a closed loop feedback on the analog part. The proposed approach was tested on EMG signals acquired from 4 subjects while performing different activities and shows that the system correctly acquires signals with no DC offset.

Power Line Interference Removal for High-Quality Continuous Biosignal Monitoring With Low-Power Wearable Devices

“…This solution achieves a large CMRR (100dB) and an SNR of 95db, which is comparable with commercial ICs for biopotential acquisition ADS1298 [30], BMD101 [31] and to state-of-the-art research solutions [32]. Moreover, Cerebro is equipped with an internal DAC used in a feedback loop to adjust the reference of each channel and remove any DC offset [33]. The platform is powered by an ARM Cortex M4 microcontroller (STM32F407) operating at a frequency of up to 168 MHz.…”

“…We implemented this method to remove the first component of the power line interference (i = 1) with f P LI = 50Hz and a window size of 1.5s as recommended in [19]. In our implementation, we do not include the DC component in the model, since Cerebro is equipped with an internal DAC used to adjust the reference of each channel and remove any DC offset [33]. The algorithm can be used in two modalities: in the first one, denoted as offline mode, the PLI is estimated and then subtracted from the same analysis window.…”