We were having a lot of trouble getting MATLAB to work on the school computer, but we managed to download it onto one of our computers. In addition, we have figured out how to gather data on Arduino. We then plotted it using the spreadsheet application on Google Drive. The picture below is just a proof of concept to show that we can gather data. The values are a sign of strength of muscle movement, and each data point is collected every 100ms. The graph is for a very long time, being about 16 and a half seconds.
Some problems we faced this week were getting Matlab to connect to Arduino on the computer to which we downloaded Matlab. For some reason it didn't work. Another problem we encountered was understanding Matlab, as looks like we will have to gain an understanding of coding and algorithms.
The plan for us is to do isolated tests of each finger for a much shorter period of time. We will repeat a specific motion, targeting a specific muscle, and through this, hopefully we will be able to identify patterns through this approach. We also hope to gain a better understanding of how to use Matlab through tutorials. We will also try to learn some coding in Arduino, although it looks like this will not be as necessary as previously thought. We have made some progress this week, we feel, and are excited to continue.
Tuesday, December 22, 2015
Sunday, December 6, 2015
STEM Patent Research: 11/30/15
An excellent list of patents, and nice summary and analysis. Based on your description in "PLACES WE CAN INNOVATE", you might want to look further into a field called Functional electrical stimulation (FES) or NeuroMuscular
Electrical Stimulation (NMES).
Tuesday, December 1, 2015
STEM Patent Research: 11/30/15
PROCESSING EMG:
Assessment of EMG signal acquired from parts of the
body where there is a lot of activation of muscles involved in motor skills
(ability to do complex muscle and nerve act to produce movement). It can
compare the pattern observed to known patterns obtained during a controlled
activity.
The invention is a circuit that can take EMG signals
and transfer them via USB to a computer.
A way and apparatus that can be used to produce a
model EMG signal from a measured one by a series of filters. The EMG signal
that results is separate from the EMG and EKG signal that is measured.
EMG signals are detected from several different
locations on the hand. These locations of electrodes are where precise
movements occur. The EMG signals are registered and processed to be used for
biometric assessment.
Acquiring uterine EMG signals. Signal processing
device transmits the signal to a relaying device which sends it out to a call
center for a doctor to look at. Can potentially help us figure out how to
transmit the signal to another arm etc. At least one pair of electrodes used.
ANALYZING EMG:
A machine learning model that has the user do specific
gestures so that it learns the signals from that gesture. The machine learning
model can then identify specific gestures from specific fingers of the user
using the information that it has learned.
A sensing device that allows patients to control an
object by using motor unit action potential. It uses an emg sensor which they
place on a specific area of the patient. It also configures a signal that
represents the motor unit action potential. Then it uses a personal area
network transmitting device that corresponds to a specific signal.
Personal area network is used to convert it to an electrical signal which then
goes through a processor where the electrical signal is received to generate at
least one control signal.
GADGETS TO HELP HUMANS:
Hybrid prosthetic arm controlled by surface EMG
(determines electrical activity of the muscle) (sEMG places electrodes on the
skin overlaying the muscle and not INTO the skin) and mechanical control from
elbow and shoulder of amputee. Device contains mechanical fingers driven by
mechanical motors controlled by microcontrollers (a small computer (SoC) on a
single integrated circuit containing a processor core, memory, and programmable
input/output peripherals.) The instruction sent to the arm is from sEMG
signals. It can convert sEMG data into instructions, and movement comes from
motion of the shoulder, rotation of the elbow, and sEMG signal.
A lower prosthetic limb that uses EMG signals to
determine the user’s specific gait phase. The device recognizes which EMG
signals correspond with which type of locomotion to better replicate the user’s
motion.
An athletic glove that records EMG signals from
electrodes on the inside of the glove. The data is gathered and processed, and
then output on an external module of the glove. This data provides feedback on
grip to the user.
TENS bandage that uses electrical stimulation to
ease and block pain within wounds. Also helps with healing of a wound.
A suit that has electrodes placed on it. Wires
connect the electrodes together with a form of stimulation device. The wearer
or user can apply electrical stimulation to certain muscle groups or parts of
the body, inhibiting a response.
STIMULATION:
Stimulating nerves using electrodes with an
electrically insulating back layer. Increases electrical current through
surrounding tissues. Increases impedance of electrical path through blood in
lumen of blood vessel.
A surface probe with a conductive tip that can apply
a local high voltage. The electricity applied causes a stimulation of the
targeted muscle fibers, eliciting a forceful movement from those muscles. The
force and number of twitches can be altered.
A device that can send electrical stimulation to the
extremities specifically. It braces the hands in place on a flat surface and
then provides the shock with a TENS unit.
An automated system that is able to deliver
electrical stimulation to the user. It can then also detect the muscle response
from the electrical stimulation. Being able to detect muscle response and
deliver electrical shocks, the system is able to automatically diagnose one
characteristic of a muscle from the response and adjust the electrical
stimulation accordingly.
A TENS device that is able to apply electrical
stimulation to muscles and also detected changes in the skin. There are two
modes, stimulation mode and re-calibration mode. Via a plurality of electrodes,
the device can apply an adjusted electrical current to the user based on skin
impedance.
A device that contains a pulse generator to mimic
MUAPs that are naturally generated. The invention can synthesize the basic wave
form, not depending on muscle contraction to achieve any form of stimulation.
A TENS unit can connect to a smartphone via
bluetooth. It can receive data as far which signals to transmit, and it can
also send data to the smartphone in response to biofeedback from the user’s
body. The controller can change pulse width, frequency, and/or intensity.
PLACES WE CAN INNOVATE:
The devices of EMG and TENS remain generally
separate in the patents that we found. Both technologies seem to be known, it
is just a matter of finding a method of combining these technologies. One place
we can innovate is in a device that integrates both technologies together.
Another area that we can innovate is in TENS. Some
of the patents that we found were able to adjust the signal, like in frequency
or intensity. We could find a way to develop a TENS unit that can accept a
changing signal and shock the user with that type of signal.
Friday, November 27, 2015
Virtual Reality Application
Hand-to-hand communication project covers two major technologies: decoding the EMG signals and encoding the TENS signals. Each technology has it's wide applications. Here shows a recent application in virtual reality (VR). Please see the YouTube video or read the article.
Tuesday, November 24, 2015
Progress Report: 11/22/15
A problem we were having was with getting the Arduino application on the computer to show the serial monitor. After a bit of trial and error, we got the monitor to load, so now we can have numerical data to use in deciphering different EMG signals. Because we have data in the form of numbers for the strength of the EMG signal, and the intervals in which the data comes, we can visualize the data, putting it into excel and creating graphs. We also found on the Backyard Brains website a pack of 100 electrodes, since we are running out of the electrodes that came with the Muscle SpikerShield Bundle.
We will need to learn how to filter out extra noise gathered from the SpikerShield. To do this we have to learn stuff about Arduino coding. We still are having trouble in gathering data, because the lights that light up on the SpikerShield seem to be inconsistent, and the electrodes also eventually lose functionality.
Our plan for the next week is to look at examples of Arduino code and gain an understanding of how we can approach filtering out noise from the signals that we gather. We also are going to work on getting real data from the SpikerShield. We are also going to look at the other patents on the subject for the patent project.
We will need to learn how to filter out extra noise gathered from the SpikerShield. To do this we have to learn stuff about Arduino coding. We still are having trouble in gathering data, because the lights that light up on the SpikerShield seem to be inconsistent, and the electrodes also eventually lose functionality.
Our plan for the next week is to look at examples of Arduino code and gain an understanding of how we can approach filtering out noise from the signals that we gather. We also are going to work on getting real data from the SpikerShield. We are also going to look at the other patents on the subject for the patent project.
Tuesday, November 17, 2015
Progress Report: 11/15/15
We analyzed out SpikerShield box. We ran a test on the SpikerShield box and tried to figure how different moments affected the light popping up on the box (The light indicated muscle activity). We looked at the Arduino code and adjusted it a little bit.
We had trouble accessing the monitor that showed our data (muscle movement) on Arduino so we had to troubleshoot and also MatLab is still not working on the computer. We might have to find a different software or spend more time on just MatLab. However, we decided to wait to work with MatLab since we need to focus on acquiring a signal.
The plan is to figure out how to display the monitor that acquires our data through Arduino. We also want to find more electrodes that we can use as our electrodes are not reusable and don't last very long.
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Here you can see the lights are lit up due to his movement. |
We had trouble accessing the monitor that showed our data (muscle movement) on Arduino so we had to troubleshoot and also MatLab is still not working on the computer. We might have to find a different software or spend more time on just MatLab. However, we decided to wait to work with MatLab since we need to focus on acquiring a signal.
The plan is to figure out how to display the monitor that acquires our data through Arduino. We also want to find more electrodes that we can use as our electrodes are not reusable and don't last very long.
Tuesday, November 10, 2015
Progress Report: 11/8/15
As far as accomplishments, we finally got the Backyard Brains Muscle SpikerShield bundle. We have been researching and examining the equipment, looking at the materials online regarding the SpikerShield. There are different experiments that they have to try out and we have been reading through them. We have also worked on installing Matlab, and have found a download that will help us to install the software and get started with Matlab. We also downloaded the software for the SpikeRecorder software from Backyard Brains, and tried to get it to function with the SpikerShield.
Some problems we faced were in installing Matlab. Due to the way the software uses the internet and the various firewalls and blocked websites that exist in the school, it is very hard to install the software. We also encountered problems with the Backyard Brains app. We plugged the SpikerShield into the computer and tried to find a way to get the app to switch from the computer microphone to the SpikerShield itself. We have been unsuccessful in it so far.
We plan to find a work around for Matlab this week, and also perform the first actual test with the SpikerShield at home on Wednesday. Because we want to minimize noise and perform the test in a controlled environment.
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