3 research outputs found
A multichannel wireless sEMG sensor endowing a 0.13 μm CMOS mixed-signal SoC
This paper presents a wireless multichannel surface electromyography (sEMG) sensor which features a custom 0.13μm CMOS mixed-signal system-on-chip (SoC) analog frontend circuit. The proposed sensor includes 10 sEMG recording channels with tunable bandwidth (BW) and analog-to-digital converter (ADC) resolution. The SoC includes 10x bioamplifiers, 10x 3 rd order ΔΣ MASH 1-1-1 ADC, and 10x on-chip decimation filters (DF). This SoC provides the sEMG samples data through a serial peripheral interface (SPI) bus to a microcontroller unit (MCU) that then transfers the data to a wireless transceiver. We report sEMG waveforms acquired using a custom multichannel electrode module, and a comparison with a commercial grade system. Results show that the proposed integrated wireless SoC-based system compares well with the commercial grade sEMG recording system. The sensor has an input-referred noise of 2.5 μVrms (BW of 10-500 Hz), an input-dynamic range of 6 mVpp, a programmable sampling rate of 2 ksps, for sEMG, while consuming only 7.1 μW/Ch for the SoC (w/ ADC & DF) and 21.8 mW of power for the sensor (Transceiver, MCU, etc.). The system lies on a 1.5 × 2.0 cm 2 printed circuit board and weights <; 1 g
Real-time hand motion recognition using sEMG Patterns Classification
Increasing
performance while decreasing the
cost of sEMG prostheses is an important milestone in
rehabilitation engineering. The
different types of
prosthetic
hands that are currently available to patients worldwide
can
benefit from more
effective and intuitive control. This paper
presents a real
-time approach to classify finger motions based
on surface electromyography (sEMG) signals. A multichannel
signal acquisition platform implemented using components of
f
the shelf is use
d to record
forearm sEMG signals
from 7
channels. sEMG pattern classification is performed in real
time, using a Linear Discriminant Analysis approach. Thirteen
hand motions can be successfully identified with an accuracy of
up to 95.
8% and
of
92.
7%
on average
for
8 participants, with
an updated prediction every 192 ms