Monday, December 28, 2015

From Arduino to MATLAB


EMG signals acquired by the SpikerShield and Arduino Uno can be send to the Macbook Air through serial port (USB). The current problem is how can we read and even display the data from MATLAB. After trying a few possible solutions from the internet. There are two working solutions you can base on for your next-step development:
  1. Serial Monitor (Debugger): It can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/45839-serial-monitor--debugger-. It is an app that you can install, and then run it by clicking the APPS tab -> MY APPS -> Serial_Monitor. It will show the serial data in a pop-up window. You can save the data to the "SerialData" variable for further processing.
  2. Real Time Serial Data Logger: It's a piece of code that you can run in the MATLAB. It can be copied from https://billwaa.wordpress.com/2013/07/10/matlab-real-time-serial-data-logger/. You can just copy and paste it into the MATLAB, and a pop-up Serial Data Log window will appear to show the plotted serial data.
The followings are the screen shots and videos of these two solutions in action.

Serial Monitor (Debugger)
 

Serial Data Logger
 

 

Finger Gestures vs. EMG Electrode Placement

Congrates to be able to gather raw EMG data from the SpikerShield! While you are picking up MATLAB, we also need to figure out the EMG electrode placement as soon as possible such that we can start acquiring meaningful data. There are three documents in our the Project Resource page, which potentially provide us the info we need.
  1. Finger Motion Decoding Using EMG Signals Corresponding Various Arm Postures (2010), Kyung‐Jin You, Ki‐Won Rhee and Hyun‐Chool Shi
  2.  Relating Forearm Muscle Electrical Activity To Finger Forces (2014), Jennifer Keating. [page 22-23, 62-84]
  3. Forearm Muscle Labels.
The simple questions we want to start with are: "In order to detect the movement (bending and stretching the finger) of individual finger,
  1. what are the most important muscle(s) related to the movement (bending and stretching) of individual fingers?
  2. How many electrodes (or electrode pairs) do we need? And,
  3. where should we place those electrodes (or electrode pairs)?"
Can your team look into them, extract useful info, and summarize the results into two tables (bending and stretching)? The tables should at least consist of the following columns: Finger, Muscles (names & symbols), Category (deep or superficial), Type (extension or flexion), Location (anterior or posterior), Electrode Placement (labels matching the diagrams), and Comments.
The exact electrode placement (position and orientation) should be labeled and indicated in the following tow diagrams (anterior view & posterior view) and matching the electrode placement in the tables.
Forearm muscles of anterior compartment: superficial, middle, and deep

Forearm muscles of posterior compartment: superficial, middle, and deep
You can contact me through email/blog whenever you have any question. Don't forget to post your results onto your blog and we will discuss our next step (data acquisition) based on the results after the break.

Tuesday, December 22, 2015

Progress Report: 11/30/15

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.

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.