Fall detector - Feature extraction

fall_detector_extract_features.py

def feature_extraction(signal_df, window_size=100, overlap=50):
    num_components = signal_df.shape[1]
    num_samples = signal_df.shape[0]

    # used to rename columns in the extracted features (mentioning the statistical moment)
    new_col_names_mean = dict()
    new_col_names_std = dict()
    new_col_names_skewness = dict()
    new_col_names_kurtosis = dict()

    for col in signal_df.columns:
        new_col_names_mean[col] = col + '_mean'
        new_col_names_std[col] = col + '_std'
        new_col_names_skewness[col] = col + '_skewness'
        new_col_names_kurtosis[col] = col + '_kurtosis'
    
    # Initialize empty DataFrames to store results
    mean_df = pd.DataFrame(columns=new_col_names_mean.values())
    std_df = pd.DataFrame(columns=new_col_names_std.values())
    skewness_df = pd.DataFrame(columns=new_col_names_skewness.values())
    kurtosis_df = pd.DataFrame(columns=new_col_names_kurtosis.values())

    # calculate number of total windows
    num_total_windows = ((num_samples - window_size) // (window_size - overlap)) + 1

    # Calculate statistical moments for each window
    for i in range(num_total_windows):
        window_start = i*(window_size - overlap)
        window = signal_df.iloc[window_start:window_start + window_size]

        # Calculate statistical moments for each component
        window_mean = window.mean()
        window_std = window.std()
        window_skewness = window.apply(skew)
        window_kurtosis = window.apply(kurtosis)

        # Append the results to the respective DataFrames
        mean_df = pd.concat([mean_df, pd.DataFrame([window_mean], columns=new_col_names_mean.values())], ignore_index=True)
        std_df = pd.concat([std_df, pd.DataFrame([window_std], columns=new_col_names_std.values())], ignore_index=True)
        skewness_df = pd.concat([skewness_df, pd.DataFrame([window_skewness], columns=new_col_names_skewness.values())], ignore_index=True)
        kurtosis_df = pd.concat([kurtosis_df, pd.DataFrame([window_kurtosis], columns=new_col_names_kurtosis.values())], ignore_index=True)

    # Combine the results into a single DataFrame
    extracted_features = pd.concat([mean_df, std_df, skewness_df, kurtosis_df], axis=1)
    
    return extracted_features