1. A 3.5mm audio jack as
well as a standard medical
equipment plug allows
the board to accept a wide
range of commercially
available EMG sensors.
Fred Buhler, Jason Hernandez, Alastair Shi, Angie Zhang
EECS 452: Mobile Monitoring of Electromyography (EMG) Activity
for Physical Therapy and Sports Medicine on Android Devices
Board is designed to
operate on a wide range
of input sources,
including batteries for
mobile use or scientific
power supplies for use in
a laboratory environment.
Power Supply
Introduction
Electromyography (EMG) signal is a complicated signal,
which is controlled by the nervous system and is dependent
on the anatomical and physiological properties of muscles.
EMG signal acquires noise while traveling through different
tissues.
Results – Displayed on Android App
Fig [1] EMG under relaxed conditions
Fig [2] After strenous activity, muscle fatigue
is indicated by a flashing screen
Custom PCB Design
DSP Algorithm Flowchart
Acknowledgments
We would like thank Professor Liu, Dr. Metzger, Armin
Sarabi and Alex Hakkola for their guidance and support.
Signal Acquisition
The PCB has self-regulated power domains, two sensor jacks, FPGA,
and a bluetooth module for connecting to an android device. The
android app allows users to download and stream live EMG data on
their mobile device via a bluetooth connection. Alternatly, the USB
port can upload data to a PC or external harware can plug into the
standard expansion header to receive real-time data.
Olimex’s open source EMG analog front end: Provided parts list
and IC calibration for amplifying low-voltage EMG signals.
• The fatigue module dectects and reports it
when the muscle is experiencing fatigue
during physical activities. (e.g. lifting a dumb
bell for acertain amoun of time )
EMG signals are studied for medical science research,
rehabilitation, ergonomics, sports science and more. EMG
is used clinically for the diagnosis of neurological and
neuromuscular problems. It is used diagnostically by gait
laboratories and by clinicians trained in the use of
biofeedback or ergonomic assessment.
• EMG allows to physicians directly “look” into the muscle
• Allows analysis to improve sports activities
Application
Literature Cited
Supporting Hardware
• The RN-42 Class 2 Bluetooth module
includes support for BCSP, DUN, LAN,
GAP SDP, RFCOMM, and L2CAP.
Most important, it connects seamlessly
to Bluetooth capable mobile devices.
• Opal Kelly’s small form-factor
XEM6001 with a Spartan6 FPGA drives
the board, is fully re-configurable, and
enables powerful Digital Signal
Processing with its DSP48A1 slices
Figure 1 and 2 are plots of live EMG data after
filtering. Figure 1 shows the EMG activities for biceps
brachii muscle group while relaxed, whereas Figure 2
shows a fatigued EMG signal. The fatigue algorithm
performs magnitude and time domain analysis on the
EMG signal and a red flashing light indicates that the
test subject’s muscle is in a state of fatigue.
Digital
Rectification
Low-Pass Filter
MA
Filter
Calibration
Module
Fatigue
Identifier
• The force output module will quantify the relationshoip between EMG
peak and force generated by the muscle and predict the force output for
an unkown weight.
Force
Output