Fall Detector - All

fall_detector_all.py

from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from scipy.stats import skew, kurtosis
from sklearn.decomposition import PCA
from sklearn.cluster import KMeans

def main():
    data = read_data()
    normalized_data = normalize(data)

    # time related columns are not needed after this point
    normalized_data = normalized_data.drop(['time','seconds_elapsed'],axis=1)
    extracted_features = feature_extraction(normalized_data)

    # dimensionality reduction using PCA
    plot_pca(extracted_features)

    # fit K-Means
    kmeans = KMeans(n_clusters=2)
    kmeans.fit(extracted_features)
    predictions = kmeans.predict(extracted_features)

    # plot the results
    plot_pca(extracted_features, predictions)
    plot_out_signal(predictions)