Project Assignment: Biomedical Computer Applications

Background:

Muscle electrical activity data, Electromyography (EMG), was collected from the lower arm muscles for 60 seconds at a sample rate of 0.001second (i.e. 1000Hz). During this time, the subject was asked to grip a force sensor at light strength for about 15 seconds, rest for a few seconds, then grip at a medium strength for 20 seconds, rest for a few seconds and then increase the grip to maximum strength for the remaining time. A grip force sensor measured the strength in Newtons.

The spreadsheet, Force_vs_EMG_Data.xlsx, file that is provided, was produced which has column A as time (seconds), column B as EMG (mV) and column C as Force data (N). LabVIEW typically uses .lvm files to store and read data. You also have been supplied with Force_vs_EMG_Data.lvm file which is identical to the excel file but it is saved in a standard text (Tab delimited) format.

The EMG data is noisy and to get any meaningful information it needs to be processed. For the first part of this project your group will need to extract meaningful data from the raw data by creating a LabVIEW VI. The purpose of this Project is to compare different data processing methods in LabVIEW. You will be using a number of screens so in order to get full marks you need to make use of Tabs in your Front Panel to help keep the output organized.

1. Write and submit a LabVIEW VI that will read in the Force_vs_EMG_Data file, either the .lvm or the excel file. There are a few different ways to read in a data file and you can select any. Some function blocks/nodes for reading in a data file can be configured to use the first column as time, and so will automatically create the "Dynamic Data" type, which will be very useful for you.

Your VI will need to perform the following data analysis:

a. Compare the force and EMG on the same axis. Because the units are so different for each you need to use the Scaling and Mapping function to produce a graph of the Force and EMG both normalized from 0 to 1. This will automatically scale each signal so that the highest
value is 1 which allow you to see both plots easily on one graph.

b. The data you have loaded contains time and two sets of corresponding data - EMG and Force. Separate (or split) the signal into EMG and Force data and then only using the EMG data, rectify it (i.e. make it all positive or take the absolute) and then examine the following effectiveness for different types of filters on the EMG signal.

i. First use a Butterworth filter from Functions Panel → Signal Processing>>Filter .

Produce a plot for a 3rd order Butterworth lowpass at 50Hz. Create a control so that the user can change the order, type (lowpass, highpass or bandpass) and the low cut off.

ii. Now use an Express Filter. This does not allow for user control while the program is running, so you will need to use three separate Express Filters with individual outputs to show the follow:

• A graph for a 3rd order Butterworth lowpass at 50Hz
• A graph for a 3rd order Inv Chebyshev lowpass at 50Hz
• A graph for Smoothing with a window of around 300. You can adjust the window size to see what works for your data.

2. Write a report that includes the above graphs for 1b9i() and 1b(ii), and then compares the results for the different filters and comment on these results.

3. When you observe the raw data, it is clear that there is some correlation between the force being applied and the EMG strength - as the grip strength goes up the EMG activity increases. You will also notice that when the subject grip strengthens on the second and third grip, there is an initial high force, which slowly decreases. However, the EMG signal does not seem to decease. This is due to muscle fatigue and because of this effect, the entire EMG cannot be assumed to be directly proportional to muscle force. For this part of the project, we will ignore the effects of fatigue and try to determine an approximate relationship between force and EMG.

To ignore the effects of fatigue, you can look at the forces for an early 5 second sample after each grip strength change, for example from 5-10 seconds, then from 20-25 seconds and finally from 45-50seconds. For these time periods, find the average force and the average rectified filtered EMG strength. Your group will need to select the most appropriate filtered EMG data and explain why you selected it.

You will now have three force and three EMG point. Using these points, draw a graph to show the relationship. Determine the mathematical relationship for these Force/EMG data points.

For maximum credit, you should create a LabVIEW VI that will achieve the above computations, however you may also choose to use any other program, like Excel. Submit to Blackboard your VI or other spreadsheet/program.

4. For the second part of your report, include the Force Vs EMG relationship graph, discuss the relationship between EMG and Force and say if and when EMG may be a useful way to measure force. Explain your answer. Say which type of rectified filtered EMG data you choose to use for this section and why.

You will need to submit to Blackboard two VI files (one for each section) or one VI file and another spreadsheet/program as well as your report. You project report must include a title page that has the team members name, what section each team member was responsible for as well as flow charts.