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.

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.

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.

Sunday, November 1, 2015

Progress Report: 11/1/15

We were able to accomplish one of the most important parts of our project this week. We were able to do all of the final product research on finding the right equipment (Backyard brains) for what we are trying to accomplish with our project. We placed the order this week and we should receive the product sometime next week. We also looked into whether or not we could use the Uno board - Leonardo instead of the Uno and it turns out that we could so we are currently working on that.

We tried to figure out Matlab by downloading the actual program onto our computer but we were having trouble setting up the program on the computer and Matlab seems to think it is a proxy error so we have to look into it and/or contact MatLab

Our plan for next week includes trouble shooting MatLab, trying out the equipment assuming it is delivered and also looking more into the program for the Arduino board.

Sunday, October 25, 2015

Progress Report: 10/25/15

We have decided that the most feasible product is something simple that has integrated software that if need be we can upgrade in the future. Looking at the brainwave groups, which likewise used simpler tools as the starting point to tackle a more advanced problem, we decided this would be the best approach. We have chosen the Muscle SpikerShield from Backyard Brains, since it is relatively cheap and can be upgraded to get up to 5 channels. We also set up an account for Matlab, a data software. 

We still need to place the order with approval from Mr Lin, and this aspect only addresses parts 1 and 2 of the project. We haven't found a TENS unit, but at the moment we are looking to just start. 

From here we will research the equipment that we have chosen, reading manuals and things before it comes. We might also look into how the Arduino works, since that is a part of our project. We should have it by next week at the latest, and once we get it we will begin to run tests on how it works.