Language Learning


Recognising sign language signs from glove sensor data

#artificialintelligence

I saved the data using numpy's np.save() function: Each row has the length 2992, which corresponds to the length of the longest recording (136) multiplied number of variables (22). I used numpy's linear interpolator to fill in the gaps randomly: This code generates a scaffold of NaN values, randomly chooses a list of indices from the scaffold which corresponds to the length of the sign being interpolated, fills the real values into the scaffold at those random indices, and then interpolates the missing values. The pipeline specifies three steps: scaling using StandardScaler(), principal components analysis (PCA) using the PCA() function and finally a linear support vector classifier (LinearSVC()). Once the PCA algorithm has reduced the dimensionality of the data to just those features which encapsulate the most variance, the data is used to train a linear support vector classification (SVC) model.