PCA on NinaPro Dataset
The NINAPRO dataset is a collection of electromyography (EMG) data that has been collected for the purpose of studying human movement and gesture recognition. The dataset consists of a series of recordings of EMG signals from a variety of different exercises performed by subjects. Exercise 1 is one of the exercises included in the dataset.
Principal component analysis (PCA) is a statistical technique that is used to reduce the dimensionality of a dataset by projecting it onto a lower-dimensional space. It does this by identifying the directions in which the data varies the most, and then projecting the data onto these directions.
The code performs PCA on the EMG data from the NINAPRO dataset, as part of Exercise 1. Specifically, it reads in the EMG data, and then applies the PCA algorithm to the data.