Question

One of the fundamental ways of controlling a hand prosthesis is to use the electromyography (EMG) signals measured from the muscles of the residual arm. The EMG signal shows the electric potential of the muscle when it is active. For example, when you flex your biceps muscle, your biceps muscle is active, and the EMG activity of the muscle can be measured.

Once we get the EMG signal, we can then apply some processing to obtain meaningful information about the muscle activation and thereby use that information to control the prosthetic hand. While the processing involves computation of different time-domain features, one commonly used time-domain feature is the root-meansquare (RMS) value.
The RMS value can be calculated using the following formula:
For an EMG signal x of length n where:
x=[x₁​,x₂​,x₃​,…,xₙ​]
RMS=√(x₁​)²+(x₂​)²+(x₃​)²+⋯(xₙ​)²​/n
​​The computation of RMS values along with other features could be used to predict the movement intent of the prosthesis user. In addition to that, for smooth prosthesis control, the relevant features must be calculated every few milliseconds. To give you an idea of this you will perform the following objectives: Your first task is implementing a program that computes the rms value of a signal (vector) using the above- mentioned formula. You will define a row or column vector of any length and compute the rms value. You are allowed to use built-in matlab functions such as 'sqrt', 'mean' and 'sum' but you should not use the built-in matlab function 'rms' to compute the RMS value in your code. You can however use the rms function to check if your implementation is working correctly.

Answer 1

The RMS value is calculated by squaring each sample of the signal, finding the mean of these squared values, and then taking the square root of the mean. This method applies to EMG signals in prosthetics, as well as in AC circuits where the average value is zero.

Step-by-step explanation:

The root mean square (RMS) value is a statistical measure used to describe the magnitude of a varying quantity, like an electromyography (EMG) signal, especially when it has both positive and negative values, such as in alternating current (AC) circuits. To compute the RMS value without using MATLAB's built-in 'rms' function, one can follow these steps:

1. Square each sample value of the signal.
2. Calculate the mean (average) of these squared values.
3. Take the square root of this average to obtain the RMS value.

This can be implemented in MATLAB or any other programming language that supports basic mathematical operations. Checking your custom RMS calculation against MATLAB's 'rms' function is a good way to ensure accuracy.