gaitsetpy.features.gait_features API documentation

# gaitsetpy.features.gait_features

Gait Feature Extractor Class Maintainer: @aharshit123456

This module contains the GaitFeatureExtractor class that inherits from BaseFeatureExtractor and provides comprehensive gait feature extraction functionality.

View Source

      1'''
      2Gait Feature Extractor Class
      3Maintainer: @aharshit123456
      4
      5This module contains the GaitFeatureExtractor class that inherits from BaseFeatureExtractor
      6and provides comprehensive gait feature extraction functionality.
      7'''
      8
      9from typing import List, Dict, Any
     10import numpy as np
     11import logging
     12from tqdm import tqdm
     13from ..core.base_classes import BaseFeatureExtractor
     14from .utils import (
     15    calculate_mean,
     16    calculate_standard_deviation,
     17    calculate_variance,
     18    calculate_skewness,
     19    calculate_kurtosis,
     20    calculate_root_mean_square,
     21    calculate_range,
     22    calculate_median,
     23    calculate_mode,
     24    calculate_mean_absolute_value,
     25    calculate_median_absolute_deviation,
     26    calculate_peak_height,
     27    calculate_stride_times,
     28    calculate_step_time,
     29    calculate_cadence,
     30    calculate_freezing_index,
     31    calculate_dominant_frequency,
     32    calculate_peak_frequency,
     33    calculate_power_spectral_entropy,
     34    calculate_principal_harmonic_frequency,
     35    calculate_entropy,
     36    calculate_interquartile_range,
     37    calculate_correlation,
     38    calculate_auto_regression_coefficients,
     39    calculate_zero_crossing_rate,
     40    calculate_energy,
     41)
     42
     43# Set up logging
     44logging.basicConfig(level=logging.INFO)
     45logger = logging.getLogger(__name__)
     46
     47
     48class GaitFeatureExtractor(BaseFeatureExtractor):
     49    """
     50    Comprehensive gait feature extractor class.
     51
     52    This class extracts various time-domain, frequency-domain, and statistical features
     53    from gait data sliding windows.
     54    """
     55
     56    def __init__(self, verbose: bool = True):
     57        super().__init__(
     58            name="gait_features",
     59            description="Comprehensive gait feature extractor for time-domain, frequency-domain, and statistical features"
     60        )
     61        self.verbose = verbose
     62        self.config = {
     63            'time_domain': True,
     64            'frequency_domain': True,
     65            'statistical': True,
     66            'ar_order': 3  # Order for auto-regression coefficients
     67        }
     68
     69        if self.verbose:
     70            print("🚀 GaitFeatureExtractor initialized successfully!")
     71            print(f"📊 Default configuration: {self.config}")
     72
     73    def extract_features(self, windows: List[Dict], fs: int, **kwargs) -> List[Dict]:
     74        """
     75        Extract gait features from sliding windows.
     76
     77        Args:
     78            windows: List of sliding window dictionaries
     79            fs: Sampling frequency
     80            **kwargs: Additional arguments including time_domain, frequency_domain, statistical flags
     81
     82        Returns:
     83            List of feature dictionaries for each sensor
     84        """
     85        # Update config with any passed arguments
     86        time_domain = kwargs.get('time_domain', self.config['time_domain'])
     87        frequency_domain = kwargs.get('frequency_domain', self.config['frequency_domain'])
     88        statistical = kwargs.get('statistical', self.config['statistical'])
     89        ar_order = kwargs.get('ar_order', self.config['ar_order'])
     90
     91        if self.verbose:
     92            print("\n" + "="*60)
     93            print("🔍 STARTING GAIT FEATURE EXTRACTION")
     94            print("="*60)
     95            print(f"📈 Total sensors/windows to process: {len(windows)}")
     96            print(f"🔊 Sampling frequency: {fs} Hz")
     97            print(f"⏱️  Time domain features: {'✅' if time_domain else '❌'}")
     98            print(f"🌊 Frequency domain features: {'✅' if frequency_domain else '❌'}")
     99            print(f"📊 Statistical features: {'✅' if statistical else '❌'}")
    100            print(f"🔄 Auto-regression order: {ar_order}")
    101            print("-"*60)
    102
    103        features = []
    104
    105        # Main progress bar for processing all windows
    106        main_pbar = tqdm(
    107            windows,
    108            desc="🔍 Processing Sensors",
    109            unit="sensor",
    110            disable=not self.verbose
    111        )
    112
    113        for i, window_dict in enumerate(main_pbar):
    114            sensor_name = window_dict['name']
    115            window_data = window_dict['data']
    116
    117            if self.verbose:
    118                main_pbar.set_postfix({
    119                    'Current': sensor_name,
    120                    'Windows': len(window_data) if isinstance(window_data, list) else 1
    121                })
    122
    123            # Skip annotation windows
    124            if sensor_name == 'annotations':
    125                if self.verbose:
    126                    logger.info(f"📝 Processing annotation data for {sensor_name}")
    127
    128                features.append({
    129                    'name': sensor_name,
    130                    'features': {},
    131                    'annotations': [self._extract_annotation_labels(window) for window in window_data]
    132                })
    133                continue
    134
    135            if self.verbose:
    136                logger.info(f"🎯 Processing sensor: {sensor_name}")
    137                logger.info(f"📦 Number of windows: {len(window_data)}")
    138
    139            sensor_features = {'name': sensor_name, 'features': {}}
    140
    141            # Time domain features
    142            if time_domain:
    143                if self.verbose:
    144                    print(f"  ⏱️  Extracting time domain features for {sensor_name}...")
    145
    146                time_features = self._extract_time_domain_features(window_data)
    147                sensor_features['features'].update(time_features)
    148
    149                if self.verbose:
    150                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in time_features.values())
    151                    print(f"  ✅ Time domain: {len(time_features)} feature types, {feature_count} total features")
    152
    153            # Frequency domain features
    154            if frequency_domain:
    155                if self.verbose:
    156                    print(f"  🌊 Extracting frequency domain features for {sensor_name}...")
    157
    158                freq_features = self._extract_frequency_domain_features(window_data, fs)
    159                sensor_features['features'].update(freq_features)
    160
    161                if self.verbose:
    162                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in freq_features.values())
    163                    print(f"  ✅ Frequency domain: {len(freq_features)} feature types, {feature_count} total features")
    164
    165            # Statistical features
    166            if statistical:
    167                if self.verbose:
    168                    print(f"  📊 Extracting statistical features for {sensor_name}...")
    169
    170                stat_features = self._extract_statistical_features(window_data)
    171                sensor_features['features'].update(stat_features)
    172
    173                if self.verbose:
    174                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in stat_features.values())
    175                    print(f"  ✅ Statistical: {len(stat_features)} feature types, {feature_count} total features")
    176
    177            # Auto-regression coefficients
    178            if self.verbose:
    179                print(f"  🔄 Extracting auto-regression coefficients for {sensor_name}...")
    180
    181            ar_features = self._extract_ar_coefficients(window_data, ar_order)
    182            sensor_features['features'].update(ar_features)
    183
    184            if self.verbose:
    185                feature_count = sum(len(v) if isinstance(v, list) else 1 for v in ar_features.values())
    186                print(f"  ✅ Auto-regression: {len(ar_features)} feature types, {feature_count} total features")
    187
    188            # Calculate total features for this sensor
    189            total_features = sum(
    190                len(v) if isinstance(v, list) else 1
    191                for v in sensor_features['features'].values()
    192            )
    193
    194            if self.verbose:
    195                print(f"  🎯 Total features extracted for {sensor_name}: {total_features}")
    196                print(f"  📋 Feature types: {list(sensor_features['features'].keys())}")
    197                print("-"*40)
    198
    199            features.append(sensor_features)
    200
    201        if self.verbose:
    202            print("\n" + "="*60)
    203            print("🎉 FEATURE EXTRACTION COMPLETED!")
    204            print("="*60)
    205            print(f"📊 Total sensors processed: {len(features)}")
    206
    207            # Calculate overall statistics
    208            total_feature_count = 0
    209            for feature_dict in features:
    210                if 'features' in feature_dict:
    211                    total_feature_count += sum(
    212                        len(v) if isinstance(v, list) else 1
    213                        for v in feature_dict['features'].values()
    214                    )
    215
    216            print(f"🔢 Total features extracted: {total_feature_count}")
    217            print(f"📈 Average features per sensor: {total_feature_count / len(features):.1f}")
    218            print("="*60)
    219
    220        return features
    221
    222    def _extract_time_domain_features(self, windows: List) -> Dict[str, List]:
    223        """Extract time domain features from windows."""
    224        if self.verbose:
    225            print("    🔍 Computing time domain features...")
    226
    227        time_features = {}
    228
    229        # Define time domain feature functions
    230        time_domain_funcs = {
    231            'mean': calculate_mean,
    232            'std': calculate_standard_deviation,
    233            'variance': calculate_variance,
    234            'rms': calculate_root_mean_square,
    235            'range': calculate_range,
    236            'median': calculate_median,
    237            'mode': calculate_mode,
    238            'mean_absolute_value': calculate_mean_absolute_value,
    239            'median_absolute_deviation': calculate_median_absolute_deviation,
    240            'peak_height': calculate_peak_height,
    241            'zero_crossing_rate': calculate_zero_crossing_rate,
    242            'energy': calculate_energy,
    243        }
    244
    245        # Progress bar for time domain features
    246        feature_pbar = tqdm(
    247            time_domain_funcs.items(),
    248            desc="    ⏱️  Time features",
    249            unit="feature",
    250            leave=False,
    251            disable=not self.verbose
    252        )
    253
    254        for feature_name, func in feature_pbar:
    255            if self.verbose:
    256                feature_pbar.set_postfix({'Computing': feature_name})
    257
    258            time_features[feature_name] = [
    259                func(self._ensure_numpy_array(window)) for window in windows
    260            ]
    261
    262        return time_features
    263
    264    def _ensure_numpy_array(self, signal):
    265        """Convert pandas Series to numpy array if needed."""
    266        if hasattr(signal, 'values'):
    267            return signal.values
    268        return signal
    269
    270    def _extract_frequency_domain_features(self, windows: List, fs: int) -> Dict[str, List]:
    271        """Extract frequency domain features from windows."""
    272        if self.verbose:
    273            print("    🔍 Computing frequency domain features...")
    274
    275        freq_features = {}
    276
    277        # Define frequency domain feature functions
    278        freq_domain_funcs = {
    279            'dominant_frequency': lambda w: calculate_dominant_frequency(w, fs),
    280            'peak_frequency': lambda w: calculate_peak_frequency(w, fs),
    281            'power_spectral_entropy': lambda w: calculate_power_spectral_entropy(w, fs),
    282            'principal_harmonic_frequency': lambda w: calculate_principal_harmonic_frequency(w, fs),
    283            'stride_times': lambda w: calculate_stride_times(w, fs),
    284            'step_time': lambda w: calculate_step_time(w, fs),
    285            'cadence': lambda w: calculate_cadence(w, fs),
    286            'freezing_index': lambda w: calculate_freezing_index(w, fs),
    287        }
    288
    289        # Progress bar for frequency domain features
    290        feature_pbar = tqdm(
    291            freq_domain_funcs.items(),
    292            desc="    🌊 Freq features",
    293            unit="feature",
    294            leave=False,
    295            disable=not self.verbose
    296        )
    297
    298        for feature_name, func in feature_pbar:
    299            if self.verbose:
    300                feature_pbar.set_postfix({'Computing': feature_name})
    301
    302            freq_features[feature_name] = [
    303                func(self._ensure_numpy_array(window)) for window in windows
    304            ]
    305
    306        return freq_features
    307
    308    def _extract_statistical_features(self, windows: List) -> Dict[str, List]:
    309        """Extract statistical features from windows."""
    310        if self.verbose:
    311            print("    🔍 Computing statistical features...")
    312
    313        stat_features = {}
    314
    315        # Define statistical feature functions
    316        stat_funcs = {
    317            'skewness': calculate_skewness,
    318            'kurtosis': calculate_kurtosis,
    319            'entropy': calculate_entropy,
    320            'interquartile_range': calculate_interquartile_range,
    321        }
    322
    323        # Progress bar for statistical features
    324        feature_pbar = tqdm(
    325            stat_funcs.items(),
    326            desc="    📊 Stat features",
    327            unit="feature",
    328            leave=False,
    329            disable=not self.verbose
    330        )
    331
    332        for feature_name, func in feature_pbar:
    333            if self.verbose:
    334                feature_pbar.set_postfix({'Computing': feature_name})
    335
    336            stat_features[feature_name] = [
    337                func(self._ensure_numpy_array(window)) for window in windows
    338            ]
    339
    340        # Handle correlation separately (needs two signals)
    341        if self.verbose:
    342            print("      🔗 Computing correlation features...")
    343
    344        stat_features['correlation'] = [
    345            calculate_correlation(
    346                self._ensure_numpy_array(window)[:-1],
    347                self._ensure_numpy_array(window)[1:]
    348            ) if len(window) > 1 else 0
    349            for window in windows
    350        ]
    351
    352        return stat_features
    353
    354    def _extract_ar_coefficients(self, windows: List, order: int) -> Dict[str, List]:
    355        """Extract auto-regression coefficients from windows."""
    356        if self.verbose:
    357            print(f"    🔍 Computing auto-regression coefficients (order={order})...")
    358
    359        # Progress bar for AR coefficients
    360        ar_pbar = tqdm(
    361            windows,
    362            desc="    🔄 AR coeffs",
    363            unit="window",
    364            leave=False,
    365            disable=not self.verbose
    366        )
    367
    368        ar_coeffs = []
    369        for window in ar_pbar:
    370            coeffs = calculate_auto_regression_coefficients(
    371                self._ensure_numpy_array(window), order
    372            )
    373            ar_coeffs.append(coeffs)
    374
    375        return {'ar_coefficients': ar_coeffs}
    376
    377    def _extract_annotation_labels(self, window) -> int:
    378        """Extract the most common annotation label from a window."""
    379        if hasattr(window, 'mode'):
    380            return window.mode().iloc[0] if len(window.mode()) > 0 else 0
    381        else:
    382            # For numpy arrays or other types
    383            unique, counts = np.unique(window, return_counts=True)
    384            return unique[np.argmax(counts)]
    385
    386    def get_feature_names(self) -> List[str]:
    387        """
    388        Get names of all features that can be extracted.
    389
    390        Returns:
    391            List of feature names
    392        """
    393        time_domain_features = [
    394            'mean', 'std', 'variance', 'rms', 'range', 'median', 'mode',
    395            'mean_absolute_value', 'median_absolute_deviation', 'peak_height',
    396            'zero_crossing_rate', 'energy'
    397        ]
    398
    399        frequency_domain_features = [
    400            'dominant_frequency', 'peak_frequency', 'power_spectral_entropy',
    401            'principal_harmonic_frequency', 'stride_times', 'step_time',
    402            'cadence', 'freezing_index'
    403        ]
    404
    405        statistical_features = [
    406            'skewness', 'kurtosis', 'entropy', 'interquartile_range', 'correlation'
    407        ]
    408
    409        other_features = ['ar_coefficients']
    410
    411        return time_domain_features + frequency_domain_features + statistical_features + other_features
    412
    413    def print_extraction_summary(self, features: List[Dict]) -> None:
    414        """
    415        Print a detailed summary of extracted features.
    416
    417        Args:
    418            features: List of feature dictionaries returned by extract_features
    419        """
    420        print("\n" + "="*80)
    421        print("📊 FEATURE EXTRACTION SUMMARY")
    422        print("="*80)
    423
    424        for i, feature_dict in enumerate(features):
    425            sensor_name = feature_dict['name']
    426            print(f"\n🎯 Sensor {i+1}: {sensor_name}")
    427            print("-" * 40)
    428
    429            if 'features' in feature_dict and feature_dict['features']:
    430                for feature_type, feature_values in feature_dict['features'].items():
    431                    if isinstance(feature_values, list):
    432                        print(f"  📈 {feature_type}: {len(feature_values)} values")
    433                        if feature_values:
    434                            sample_value = feature_values[0]
    435                            if isinstance(sample_value, (list, np.ndarray)):
    436                                print(f"    └── Shape per window: {np.array(sample_value).shape}")
    437                            else:
    438                                print(f"    └── Sample value: {sample_value:.4f}")
    439                    else:
    440                        print(f"  📈 {feature_type}: {feature_values}")
    441
    442            if 'annotations' in feature_dict:
    443                print(f"  📝 Annotations: {len(feature_dict['annotations'])} windows")
    444
    445        print("\n" + "="*80)
    446
    447
    448# Legacy function wrappers for backward compatibility
    449def get_stride_times_for_windows(windows, fs):
    450    """Calculate stride times for all windows in the input."""
    451    return [calculate_stride_times(window, fs) for window in windows]
    452
    453def get_zero_crossing_rates_for_windows(windows):
    454    """Calculate zero-crossing rates for all windows in the input."""
    455    return [calculate_zero_crossing_rate(window) for window in windows]
    456
    457def get_freezing_indices_for_windows(windows, fs):
    458    """Calculate freezing indices for all windows in the input."""
    459    return [calculate_freezing_index(window, fs) for window in windows]
    460
    461def get_standard_deviations_for_windows(windows):
    462    """Calculate standard deviations for all windows in the input."""
    463    return [calculate_standard_deviation(window) for window in windows]
    464
    465def get_entropies_for_windows(windows):
    466    """Calculate entropies for all windows in the input."""
    467    return [calculate_entropy(window) for window in windows]
    468
    469def get_energies_for_windows(windows):
    470    """Calculate energies for all windows in the input."""
    471    return [calculate_energy(window) for window in windows]
    472
    473def get_variances_for_windows(windows):
    474    """Calculate variances for all windows in the input."""
    475    return [calculate_variance(window) for window in windows]
    476
    477def get_kurtosis_for_windows(windows):
    478    """Calculate kurtosis values for all windows in the input."""
    479    return [calculate_kurtosis(window) for window in windows]
    480
    481def get_step_times_for_windows(windows, fs):
    482    """Calculate step times for all windows in the input."""
    483    return [calculate_step_time(window, fs) for window in windows]
    484
    485def get_mean_for_windows(windows):
    486    return [calculate_mean(window) for window in windows]
    487
    488def get_standard_deviation_for_windows(windows):
    489    return [calculate_standard_deviation(window) for window in windows]
    490
    491def get_variance_for_windows(windows):
    492    return [calculate_variance(window) for window in windows]
    493
    494def get_skewness_for_windows(windows):
    495    return [calculate_skewness(window) for window in windows]
    496
    497def get_root_mean_square_for_windows(windows):
    498    return [calculate_root_mean_square(window) for window in windows]
    499
    500def get_range_for_windows(windows):
    501    return [calculate_range(window) for window in windows]
    502
    503def get_median_for_windows(windows):
    504    return [calculate_median(window) for window in windows]
    505
    506def get_mode_for_windows(windows):
    507    return [calculate_mode(window) for window in windows]
    508
    509def get_mean_absolute_value_for_windows(windows):
    510    return [calculate_mean_absolute_value(window) for window in windows]
    511
    512def get_median_absolute_deviation_for_windows(windows):
    513    return [calculate_median_absolute_deviation(window) for window in windows]
    514
    515def get_peak_height_for_windows(windows):
    516    return [calculate_peak_height(window) for window in windows]
    517
    518def get_cadence_for_windows(windows, fs):
    519    return [calculate_cadence(window, fs) for window in windows]
    520
    521def get_freezing_index_for_windows(windows, fs):
    522    return [calculate_freezing_index(window, fs) for window in windows]
    523
    524def get_dominant_frequency_for_windows(windows, fs):
    525    return [calculate_dominant_frequency(window, fs) for window in windows]
    526
    527def get_peak_frequency_for_windows(windows, fs):
    528    return [calculate_peak_frequency(window, fs) for window in windows]
    529
    530def get_power_spectral_entropy_for_windows(windows, fs):
    531    return [calculate_power_spectral_entropy(window, fs) for window in windows]
    532
    533def get_principal_harmonic_frequency_for_windows(windows, fs):
    534    return [calculate_principal_harmonic_frequency(window, fs) for window in windows]
    535
    536def get_entropy_for_windows(windows):
    537    return [calculate_entropy(window) for window in windows]
    538
    539def get_interquartile_range_for_windows(windows):
    540    return [calculate_interquartile_range(window) for window in windows]
    541
    542def get_correlation_for_windows(windows):
    543    # For correlation, we need to handle it differently since it needs two signals
    544    # We'll calculate autocorrelation for each window
    545    return [calculate_correlation(window[:-1], window[1:]) if len(window) > 1 else 0 for window in windows]
    546
    547def get_auto_regression_coefficients_for_windows(windows, order=3):
    548    return [calculate_auto_regression_coefficients(window, order) for window in windows]
    549
    550def extract_gait_features(daphnet_windows, fs, time_domain=True, frequency_domain=True, statistical=True, verbose=True):
    551    """
    552    Legacy function for extracting gait features.
    553
    554    Args:
    555        daphnet_windows: List of sliding window dictionaries
    556        fs: Sampling frequency
    557        time_domain: Whether to extract time domain features
    558        frequency_domain: Whether to extract frequency domain features
    559        statistical: Whether to extract statistical features
    560        verbose: Whether to show verbose output and progress bars
    561
    562    Returns:
    563        List of feature dictionaries
    564    """
    565    extractor = GaitFeatureExtractor(verbose=verbose)
    566    return extractor.extract_features(
    567        daphnet_windows, fs,
    568        time_domain=time_domain,
    569        frequency_domain=frequency_domain,
    570        statistical=statistical
    571    )

logger = <Logger gaitsetpy.features.gait_features (INFO)>
class GaitFeatureExtractor(gaitsetpy.core.base_classes.BaseFeatureExtractor): View Source

     49class GaitFeatureExtractor(BaseFeatureExtractor):
     50    """
     51    Comprehensive gait feature extractor class.
     52
     53    This class extracts various time-domain, frequency-domain, and statistical features
     54    from gait data sliding windows.
     55    """
     56
     57    def __init__(self, verbose: bool = True):
     58        super().__init__(
     59            name="gait_features",
     60            description="Comprehensive gait feature extractor for time-domain, frequency-domain, and statistical features"
     61        )
     62        self.verbose = verbose
     63        self.config = {
     64            'time_domain': True,
     65            'frequency_domain': True,
     66            'statistical': True,
     67            'ar_order': 3  # Order for auto-regression coefficients
     68        }
     69
     70        if self.verbose:
     71            print("🚀 GaitFeatureExtractor initialized successfully!")
     72            print(f"📊 Default configuration: {self.config}")
     73
     74    def extract_features(self, windows: List[Dict], fs: int, **kwargs) -> List[Dict]:
     75        """
     76        Extract gait features from sliding windows.
     77
     78        Args:
     79            windows: List of sliding window dictionaries
     80            fs: Sampling frequency
     81            **kwargs: Additional arguments including time_domain, frequency_domain, statistical flags
     82
     83        Returns:
     84            List of feature dictionaries for each sensor
     85        """
     86        # Update config with any passed arguments
     87        time_domain = kwargs.get('time_domain', self.config['time_domain'])
     88        frequency_domain = kwargs.get('frequency_domain', self.config['frequency_domain'])
     89        statistical = kwargs.get('statistical', self.config['statistical'])
     90        ar_order = kwargs.get('ar_order', self.config['ar_order'])
     91
     92        if self.verbose:
     93            print("\n" + "="*60)
     94            print("🔍 STARTING GAIT FEATURE EXTRACTION")
     95            print("="*60)
     96            print(f"📈 Total sensors/windows to process: {len(windows)}")
     97            print(f"🔊 Sampling frequency: {fs} Hz")
     98            print(f"⏱️  Time domain features: {'✅' if time_domain else '❌'}")
     99            print(f"🌊 Frequency domain features: {'✅' if frequency_domain else '❌'}")
    100            print(f"📊 Statistical features: {'✅' if statistical else '❌'}")
    101            print(f"🔄 Auto-regression order: {ar_order}")
    102            print("-"*60)
    103
    104        features = []
    105
    106        # Main progress bar for processing all windows
    107        main_pbar = tqdm(
    108            windows,
    109            desc="🔍 Processing Sensors",
    110            unit="sensor",
    111            disable=not self.verbose
    112        )
    113
    114        for i, window_dict in enumerate(main_pbar):
    115            sensor_name = window_dict['name']
    116            window_data = window_dict['data']
    117
    118            if self.verbose:
    119                main_pbar.set_postfix({
    120                    'Current': sensor_name,
    121                    'Windows': len(window_data) if isinstance(window_data, list) else 1
    122                })
    123
    124            # Skip annotation windows
    125            if sensor_name == 'annotations':
    126                if self.verbose:
    127                    logger.info(f"📝 Processing annotation data for {sensor_name}")
    128
    129                features.append({
    130                    'name': sensor_name,
    131                    'features': {},
    132                    'annotations': [self._extract_annotation_labels(window) for window in window_data]
    133                })
    134                continue
    135
    136            if self.verbose:
    137                logger.info(f"🎯 Processing sensor: {sensor_name}")
    138                logger.info(f"📦 Number of windows: {len(window_data)}")
    139
    140            sensor_features = {'name': sensor_name, 'features': {}}
    141
    142            # Time domain features
    143            if time_domain:
    144                if self.verbose:
    145                    print(f"  ⏱️  Extracting time domain features for {sensor_name}...")
    146
    147                time_features = self._extract_time_domain_features(window_data)
    148                sensor_features['features'].update(time_features)
    149
    150                if self.verbose:
    151                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in time_features.values())
    152                    print(f"  ✅ Time domain: {len(time_features)} feature types, {feature_count} total features")
    153
    154            # Frequency domain features
    155            if frequency_domain:
    156                if self.verbose:
    157                    print(f"  🌊 Extracting frequency domain features for {sensor_name}...")
    158
    159                freq_features = self._extract_frequency_domain_features(window_data, fs)
    160                sensor_features['features'].update(freq_features)
    161
    162                if self.verbose:
    163                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in freq_features.values())
    164                    print(f"  ✅ Frequency domain: {len(freq_features)} feature types, {feature_count} total features")
    165
    166            # Statistical features
    167            if statistical:
    168                if self.verbose:
    169                    print(f"  📊 Extracting statistical features for {sensor_name}...")
    170
    171                stat_features = self._extract_statistical_features(window_data)
    172                sensor_features['features'].update(stat_features)
    173
    174                if self.verbose:
    175                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in stat_features.values())
    176                    print(f"  ✅ Statistical: {len(stat_features)} feature types, {feature_count} total features")
    177
    178            # Auto-regression coefficients
    179            if self.verbose:
    180                print(f"  🔄 Extracting auto-regression coefficients for {sensor_name}...")
    181
    182            ar_features = self._extract_ar_coefficients(window_data, ar_order)
    183            sensor_features['features'].update(ar_features)
    184
    185            if self.verbose:
    186                feature_count = sum(len(v) if isinstance(v, list) else 1 for v in ar_features.values())
    187                print(f"  ✅ Auto-regression: {len(ar_features)} feature types, {feature_count} total features")
    188
    189            # Calculate total features for this sensor
    190            total_features = sum(
    191                len(v) if isinstance(v, list) else 1
    192                for v in sensor_features['features'].values()
    193            )
    194
    195            if self.verbose:
    196                print(f"  🎯 Total features extracted for {sensor_name}: {total_features}")
    197                print(f"  📋 Feature types: {list(sensor_features['features'].keys())}")
    198                print("-"*40)
    199
    200            features.append(sensor_features)
    201
    202        if self.verbose:
    203            print("\n" + "="*60)
    204            print("🎉 FEATURE EXTRACTION COMPLETED!")
    205            print("="*60)
    206            print(f"📊 Total sensors processed: {len(features)}")
    207
    208            # Calculate overall statistics
    209            total_feature_count = 0
    210            for feature_dict in features:
    211                if 'features' in feature_dict:
    212                    total_feature_count += sum(
    213                        len(v) if isinstance(v, list) else 1
    214                        for v in feature_dict['features'].values()
    215                    )
    216
    217            print(f"🔢 Total features extracted: {total_feature_count}")
    218            print(f"📈 Average features per sensor: {total_feature_count / len(features):.1f}")
    219            print("="*60)
    220
    221        return features
    222
    223    def _extract_time_domain_features(self, windows: List) -> Dict[str, List]:
    224        """Extract time domain features from windows."""
    225        if self.verbose:
    226            print("    🔍 Computing time domain features...")
    227
    228        time_features = {}
    229
    230        # Define time domain feature functions
    231        time_domain_funcs = {
    232            'mean': calculate_mean,
    233            'std': calculate_standard_deviation,
    234            'variance': calculate_variance,
    235            'rms': calculate_root_mean_square,
    236            'range': calculate_range,
    237            'median': calculate_median,
    238            'mode': calculate_mode,
    239            'mean_absolute_value': calculate_mean_absolute_value,
    240            'median_absolute_deviation': calculate_median_absolute_deviation,
    241            'peak_height': calculate_peak_height,
    242            'zero_crossing_rate': calculate_zero_crossing_rate,
    243            'energy': calculate_energy,
    244        }
    245
    246        # Progress bar for time domain features
    247        feature_pbar = tqdm(
    248            time_domain_funcs.items(),
    249            desc="    ⏱️  Time features",
    250            unit="feature",
    251            leave=False,
    252            disable=not self.verbose
    253        )
    254
    255        for feature_name, func in feature_pbar:
    256            if self.verbose:
    257                feature_pbar.set_postfix({'Computing': feature_name})
    258
    259            time_features[feature_name] = [
    260                func(self._ensure_numpy_array(window)) for window in windows
    261            ]
    262
    263        return time_features
    264
    265    def _ensure_numpy_array(self, signal):
    266        """Convert pandas Series to numpy array if needed."""
    267        if hasattr(signal, 'values'):
    268            return signal.values
    269        return signal
    270
    271    def _extract_frequency_domain_features(self, windows: List, fs: int) -> Dict[str, List]:
    272        """Extract frequency domain features from windows."""
    273        if self.verbose:
    274            print("    🔍 Computing frequency domain features...")
    275
    276        freq_features = {}
    277
    278        # Define frequency domain feature functions
    279        freq_domain_funcs = {
    280            'dominant_frequency': lambda w: calculate_dominant_frequency(w, fs),
    281            'peak_frequency': lambda w: calculate_peak_frequency(w, fs),
    282            'power_spectral_entropy': lambda w: calculate_power_spectral_entropy(w, fs),
    283            'principal_harmonic_frequency': lambda w: calculate_principal_harmonic_frequency(w, fs),
    284            'stride_times': lambda w: calculate_stride_times(w, fs),
    285            'step_time': lambda w: calculate_step_time(w, fs),
    286            'cadence': lambda w: calculate_cadence(w, fs),
    287            'freezing_index': lambda w: calculate_freezing_index(w, fs),
    288        }
    289
    290        # Progress bar for frequency domain features
    291        feature_pbar = tqdm(
    292            freq_domain_funcs.items(),
    293            desc="    🌊 Freq features",
    294            unit="feature",
    295            leave=False,
    296            disable=not self.verbose
    297        )
    298
    299        for feature_name, func in feature_pbar:
    300            if self.verbose:
    301                feature_pbar.set_postfix({'Computing': feature_name})
    302
    303            freq_features[feature_name] = [
    304                func(self._ensure_numpy_array(window)) for window in windows
    305            ]
    306
    307        return freq_features
    308
    309    def _extract_statistical_features(self, windows: List) -> Dict[str, List]:
    310        """Extract statistical features from windows."""
    311        if self.verbose:
    312            print("    🔍 Computing statistical features...")
    313
    314        stat_features = {}
    315
    316        # Define statistical feature functions
    317        stat_funcs = {
    318            'skewness': calculate_skewness,
    319            'kurtosis': calculate_kurtosis,
    320            'entropy': calculate_entropy,
    321            'interquartile_range': calculate_interquartile_range,
    322        }
    323
    324        # Progress bar for statistical features
    325        feature_pbar = tqdm(
    326            stat_funcs.items(),
    327            desc="    📊 Stat features",
    328            unit="feature",
    329            leave=False,
    330            disable=not self.verbose
    331        )
    332
    333        for feature_name, func in feature_pbar:
    334            if self.verbose:
    335                feature_pbar.set_postfix({'Computing': feature_name})
    336
    337            stat_features[feature_name] = [
    338                func(self._ensure_numpy_array(window)) for window in windows
    339            ]
    340
    341        # Handle correlation separately (needs two signals)
    342        if self.verbose:
    343            print("      🔗 Computing correlation features...")
    344
    345        stat_features['correlation'] = [
    346            calculate_correlation(
    347                self._ensure_numpy_array(window)[:-1],
    348                self._ensure_numpy_array(window)[1:]
    349            ) if len(window) > 1 else 0
    350            for window in windows
    351        ]
    352
    353        return stat_features
    354
    355    def _extract_ar_coefficients(self, windows: List, order: int) -> Dict[str, List]:
    356        """Extract auto-regression coefficients from windows."""
    357        if self.verbose:
    358            print(f"    🔍 Computing auto-regression coefficients (order={order})...")
    359
    360        # Progress bar for AR coefficients
    361        ar_pbar = tqdm(
    362            windows,
    363            desc="    🔄 AR coeffs",
    364            unit="window",
    365            leave=False,
    366            disable=not self.verbose
    367        )
    368
    369        ar_coeffs = []
    370        for window in ar_pbar:
    371            coeffs = calculate_auto_regression_coefficients(
    372                self._ensure_numpy_array(window), order
    373            )
    374            ar_coeffs.append(coeffs)
    375
    376        return {'ar_coefficients': ar_coeffs}
    377
    378    def _extract_annotation_labels(self, window) -> int:
    379        """Extract the most common annotation label from a window."""
    380        if hasattr(window, 'mode'):
    381            return window.mode().iloc[0] if len(window.mode()) > 0 else 0
    382        else:
    383            # For numpy arrays or other types
    384            unique, counts = np.unique(window, return_counts=True)
    385            return unique[np.argmax(counts)]
    386
    387    def get_feature_names(self) -> List[str]:
    388        """
    389        Get names of all features that can be extracted.
    390
    391        Returns:
    392            List of feature names
    393        """
    394        time_domain_features = [
    395            'mean', 'std', 'variance', 'rms', 'range', 'median', 'mode',
    396            'mean_absolute_value', 'median_absolute_deviation', 'peak_height',
    397            'zero_crossing_rate', 'energy'
    398        ]
    399
    400        frequency_domain_features = [
    401            'dominant_frequency', 'peak_frequency', 'power_spectral_entropy',
    402            'principal_harmonic_frequency', 'stride_times', 'step_time',
    403            'cadence', 'freezing_index'
    404        ]
    405
    406        statistical_features = [
    407            'skewness', 'kurtosis', 'entropy', 'interquartile_range', 'correlation'
    408        ]
    409
    410        other_features = ['ar_coefficients']
    411
    412        return time_domain_features + frequency_domain_features + statistical_features + other_features
    413
    414    def print_extraction_summary(self, features: List[Dict]) -> None:
    415        """
    416        Print a detailed summary of extracted features.
    417
    418        Args:
    419            features: List of feature dictionaries returned by extract_features
    420        """
    421        print("\n" + "="*80)
    422        print("📊 FEATURE EXTRACTION SUMMARY")
    423        print("="*80)
    424
    425        for i, feature_dict in enumerate(features):
    426            sensor_name = feature_dict['name']
    427            print(f"\n🎯 Sensor {i+1}: {sensor_name}")
    428            print("-" * 40)
    429
    430            if 'features' in feature_dict and feature_dict['features']:
    431                for feature_type, feature_values in feature_dict['features'].items():
    432                    if isinstance(feature_values, list):
    433                        print(f"  📈 {feature_type}: {len(feature_values)} values")
    434                        if feature_values:
    435                            sample_value = feature_values[0]
    436                            if isinstance(sample_value, (list, np.ndarray)):
    437                                print(f"    └── Shape per window: {np.array(sample_value).shape}")
    438                            else:
    439                                print(f"    └── Sample value: {sample_value:.4f}")
    440                    else:
    441                        print(f"  📈 {feature_type}: {feature_values}")
    442
    443            if 'annotations' in feature_dict:
    444                print(f"  📝 Annotations: {len(feature_dict['annotations'])} windows")
    445
    446        print("\n" + "="*80)

Comprehensive gait feature extractor class.

This class extracts various time-domain, frequency-domain, and statistical features from gait data sliding windows.

GaitFeatureExtractor(verbose: bool = True) View Source

    57    def __init__(self, verbose: bool = True):
    58        super().__init__(
    59            name="gait_features",
    60            description="Comprehensive gait feature extractor for time-domain, frequency-domain, and statistical features"
    61        )
    62        self.verbose = verbose
    63        self.config = {
    64            'time_domain': True,
    65            'frequency_domain': True,
    66            'statistical': True,
    67            'ar_order': 3  # Order for auto-regression coefficients
    68        }
    69
    70        if self.verbose:
    71            print("🚀 GaitFeatureExtractor initialized successfully!")
    72            print(f"📊 Default configuration: {self.config}")

Initialize the feature extractor.

Args: name: Name of the feature extractor description: Description of the feature extractor

verbose
config
def extract_features(self, windows: List[Dict], fs: int, **kwargs) -> List[Dict]: View Source

     74    def extract_features(self, windows: List[Dict], fs: int, **kwargs) -> List[Dict]:
     75        """
     76        Extract gait features from sliding windows.
     77
     78        Args:
     79            windows: List of sliding window dictionaries
     80            fs: Sampling frequency
     81            **kwargs: Additional arguments including time_domain, frequency_domain, statistical flags
     82
     83        Returns:
     84            List of feature dictionaries for each sensor
     85        """
     86        # Update config with any passed arguments
     87        time_domain = kwargs.get('time_domain', self.config['time_domain'])
     88        frequency_domain = kwargs.get('frequency_domain', self.config['frequency_domain'])
     89        statistical = kwargs.get('statistical', self.config['statistical'])
     90        ar_order = kwargs.get('ar_order', self.config['ar_order'])
     91
     92        if self.verbose:
     93            print("\n" + "="*60)
     94            print("🔍 STARTING GAIT FEATURE EXTRACTION")
     95            print("="*60)
     96            print(f"📈 Total sensors/windows to process: {len(windows)}")
     97            print(f"🔊 Sampling frequency: {fs} Hz")
     98            print(f"⏱️  Time domain features: {'✅' if time_domain else '❌'}")
     99            print(f"🌊 Frequency domain features: {'✅' if frequency_domain else '❌'}")
    100            print(f"📊 Statistical features: {'✅' if statistical else '❌'}")
    101            print(f"🔄 Auto-regression order: {ar_order}")
    102            print("-"*60)
    103
    104        features = []
    105
    106        # Main progress bar for processing all windows
    107        main_pbar = tqdm(
    108            windows,
    109            desc="🔍 Processing Sensors",
    110            unit="sensor",
    111            disable=not self.verbose
    112        )
    113
    114        for i, window_dict in enumerate(main_pbar):
    115            sensor_name = window_dict['name']
    116            window_data = window_dict['data']
    117
    118            if self.verbose:
    119                main_pbar.set_postfix({
    120                    'Current': sensor_name,
    121                    'Windows': len(window_data) if isinstance(window_data, list) else 1
    122                })
    123
    124            # Skip annotation windows
    125            if sensor_name == 'annotations':
    126                if self.verbose:
    127                    logger.info(f"📝 Processing annotation data for {sensor_name}")
    128
    129                features.append({
    130                    'name': sensor_name,
    131                    'features': {},
    132                    'annotations': [self._extract_annotation_labels(window) for window in window_data]
    133                })
    134                continue
    135
    136            if self.verbose:
    137                logger.info(f"🎯 Processing sensor: {sensor_name}")
    138                logger.info(f"📦 Number of windows: {len(window_data)}")
    139
    140            sensor_features = {'name': sensor_name, 'features': {}}
    141
    142            # Time domain features
    143            if time_domain:
    144                if self.verbose:
    145                    print(f"  ⏱️  Extracting time domain features for {sensor_name}...")
    146
    147                time_features = self._extract_time_domain_features(window_data)
    148                sensor_features['features'].update(time_features)
    149
    150                if self.verbose:
    151                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in time_features.values())
    152                    print(f"  ✅ Time domain: {len(time_features)} feature types, {feature_count} total features")
    153
    154            # Frequency domain features
    155            if frequency_domain:
    156                if self.verbose:
    157                    print(f"  🌊 Extracting frequency domain features for {sensor_name}...")
    158
    159                freq_features = self._extract_frequency_domain_features(window_data, fs)
    160                sensor_features['features'].update(freq_features)
    161
    162                if self.verbose:
    163                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in freq_features.values())
    164                    print(f"  ✅ Frequency domain: {len(freq_features)} feature types, {feature_count} total features")
    165
    166            # Statistical features
    167            if statistical:
    168                if self.verbose:
    169                    print(f"  📊 Extracting statistical features for {sensor_name}...")
    170
    171                stat_features = self._extract_statistical_features(window_data)
    172                sensor_features['features'].update(stat_features)
    173
    174                if self.verbose:
    175                    feature_count = sum(len(v) if isinstance(v, list) else 1 for v in stat_features.values())
    176                    print(f"  ✅ Statistical: {len(stat_features)} feature types, {feature_count} total features")
    177
    178            # Auto-regression coefficients
    179            if self.verbose:
    180                print(f"  🔄 Extracting auto-regression coefficients for {sensor_name}...")
    181
    182            ar_features = self._extract_ar_coefficients(window_data, ar_order)
    183            sensor_features['features'].update(ar_features)
    184
    185            if self.verbose:
    186                feature_count = sum(len(v) if isinstance(v, list) else 1 for v in ar_features.values())
    187                print(f"  ✅ Auto-regression: {len(ar_features)} feature types, {feature_count} total features")
    188
    189            # Calculate total features for this sensor
    190            total_features = sum(
    191                len(v) if isinstance(v, list) else 1
    192                for v in sensor_features['features'].values()
    193            )
    194
    195            if self.verbose:
    196                print(f"  🎯 Total features extracted for {sensor_name}: {total_features}")
    197                print(f"  📋 Feature types: {list(sensor_features['features'].keys())}")
    198                print("-"*40)
    199
    200            features.append(sensor_features)
    201
    202        if self.verbose:
    203            print("\n" + "="*60)
    204            print("🎉 FEATURE EXTRACTION COMPLETED!")
    205            print("="*60)
    206            print(f"📊 Total sensors processed: {len(features)}")
    207
    208            # Calculate overall statistics
    209            total_feature_count = 0
    210            for feature_dict in features:
    211                if 'features' in feature_dict:
    212                    total_feature_count += sum(
    213                        len(v) if isinstance(v, list) else 1
    214                        for v in feature_dict['features'].values()
    215                    )
    216
    217            print(f"🔢 Total features extracted: {total_feature_count}")
    218            print(f"📈 Average features per sensor: {total_feature_count / len(features):.1f}")
    219            print("="*60)
    220
    221        return features

Extract gait features from sliding windows.

Args: windows: List of sliding window dictionaries fs: Sampling frequency **kwargs: Additional arguments including time_domain, frequency_domain, statistical flags

Returns: List of feature dictionaries for each sensor

def get_feature_names(self) -> List[str]: View Source

    387    def get_feature_names(self) -> List[str]:
    388        """
    389        Get names of all features that can be extracted.
    390
    391        Returns:
    392            List of feature names
    393        """
    394        time_domain_features = [
    395            'mean', 'std', 'variance', 'rms', 'range', 'median', 'mode',
    396            'mean_absolute_value', 'median_absolute_deviation', 'peak_height',
    397            'zero_crossing_rate', 'energy'
    398        ]
    399
    400        frequency_domain_features = [
    401            'dominant_frequency', 'peak_frequency', 'power_spectral_entropy',
    402            'principal_harmonic_frequency', 'stride_times', 'step_time',
    403            'cadence', 'freezing_index'
    404        ]
    405
    406        statistical_features = [
    407            'skewness', 'kurtosis', 'entropy', 'interquartile_range', 'correlation'
    408        ]
    409
    410        other_features = ['ar_coefficients']
    411
    412        return time_domain_features + frequency_domain_features + statistical_features + other_features

Get names of all features that can be extracted.

Returns: List of feature names

def print_extraction_summary(self, features: List[Dict]) -> None: View Source

    414    def print_extraction_summary(self, features: List[Dict]) -> None:
    415        """
    416        Print a detailed summary of extracted features.
    417
    418        Args:
    419            features: List of feature dictionaries returned by extract_features
    420        """
    421        print("\n" + "="*80)
    422        print("📊 FEATURE EXTRACTION SUMMARY")
    423        print("="*80)
    424
    425        for i, feature_dict in enumerate(features):
    426            sensor_name = feature_dict['name']
    427            print(f"\n🎯 Sensor {i+1}: {sensor_name}")
    428            print("-" * 40)
    429
    430            if 'features' in feature_dict and feature_dict['features']:
    431                for feature_type, feature_values in feature_dict['features'].items():
    432                    if isinstance(feature_values, list):
    433                        print(f"  📈 {feature_type}: {len(feature_values)} values")
    434                        if feature_values:
    435                            sample_value = feature_values[0]
    436                            if isinstance(sample_value, (list, np.ndarray)):
    437                                print(f"    └── Shape per window: {np.array(sample_value).shape}")
    438                            else:
    439                                print(f"    └── Sample value: {sample_value:.4f}")
    440                    else:
    441                        print(f"  📈 {feature_type}: {feature_values}")
    442
    443            if 'annotations' in feature_dict:
    444                print(f"  📝 Annotations: {len(feature_dict['annotations'])} windows")
    445
    446        print("\n" + "="*80)

Print a detailed summary of extracted features.

Args: features: List of feature dictionaries returned by extract_features

##### Inherited Members

gaitsetpy.core.base_classes.BaseFeatureExtractor
  name
  description
  configure
  get_info

def get_stride_times_for_windows(windows, fs): View Source

    450def get_stride_times_for_windows(windows, fs):
    451    """Calculate stride times for all windows in the input."""
    452    return [calculate_stride_times(window, fs) for window in windows]

Calculate stride times for all windows in the input.

def get_zero_crossing_rates_for_windows(windows): View Source

    454def get_zero_crossing_rates_for_windows(windows):
    455    """Calculate zero-crossing rates for all windows in the input."""
    456    return [calculate_zero_crossing_rate(window) for window in windows]

Calculate zero-crossing rates for all windows in the input.

def get_freezing_indices_for_windows(windows, fs): View Source

    458def get_freezing_indices_for_windows(windows, fs):
    459    """Calculate freezing indices for all windows in the input."""
    460    return [calculate_freezing_index(window, fs) for window in windows]

Calculate freezing indices for all windows in the input.

def get_standard_deviations_for_windows(windows): View Source

    462def get_standard_deviations_for_windows(windows):
    463    """Calculate standard deviations for all windows in the input."""
    464    return [calculate_standard_deviation(window) for window in windows]

Calculate standard deviations for all windows in the input.

def get_entropies_for_windows(windows): View Source

    466def get_entropies_for_windows(windows):
    467    """Calculate entropies for all windows in the input."""
    468    return [calculate_entropy(window) for window in windows]

Calculate entropies for all windows in the input.

def get_energies_for_windows(windows): View Source

    470def get_energies_for_windows(windows):
    471    """Calculate energies for all windows in the input."""
    472    return [calculate_energy(window) for window in windows]

Calculate energies for all windows in the input.

def get_variances_for_windows(windows): View Source

    474def get_variances_for_windows(windows):
    475    """Calculate variances for all windows in the input."""
    476    return [calculate_variance(window) for window in windows]

Calculate variances for all windows in the input.

def get_kurtosis_for_windows(windows): View Source

    478def get_kurtosis_for_windows(windows):
    479    """Calculate kurtosis values for all windows in the input."""
    480    return [calculate_kurtosis(window) for window in windows]

Calculate kurtosis values for all windows in the input.

def get_step_times_for_windows(windows, fs): View Source

    482def get_step_times_for_windows(windows, fs):
    483    """Calculate step times for all windows in the input."""
    484    return [calculate_step_time(window, fs) for window in windows]

Calculate step times for all windows in the input.

def get_mean_for_windows(windows): View Source

    486def get_mean_for_windows(windows):
    487    return [calculate_mean(window) for window in windows]

def get_standard_deviation_for_windows(windows): View Source

    489def get_standard_deviation_for_windows(windows):
    490    return [calculate_standard_deviation(window) for window in windows]

def get_variance_for_windows(windows): View Source

    492def get_variance_for_windows(windows):
    493    return [calculate_variance(window) for window in windows]

def get_skewness_for_windows(windows): View Source

    495def get_skewness_for_windows(windows):
    496    return [calculate_skewness(window) for window in windows]

def get_root_mean_square_for_windows(windows): View Source

    498def get_root_mean_square_for_windows(windows):
    499    return [calculate_root_mean_square(window) for window in windows]

def get_range_for_windows(windows): View Source

    501def get_range_for_windows(windows):
    502    return [calculate_range(window) for window in windows]

def get_median_for_windows(windows): View Source

    504def get_median_for_windows(windows):
    505    return [calculate_median(window) for window in windows]

def get_mode_for_windows(windows): View Source

    507def get_mode_for_windows(windows):
    508    return [calculate_mode(window) for window in windows]

def get_mean_absolute_value_for_windows(windows): View Source

    510def get_mean_absolute_value_for_windows(windows):
    511    return [calculate_mean_absolute_value(window) for window in windows]

def get_median_absolute_deviation_for_windows(windows): View Source

    513def get_median_absolute_deviation_for_windows(windows):
    514    return [calculate_median_absolute_deviation(window) for window in windows]

def get_peak_height_for_windows(windows): View Source

    516def get_peak_height_for_windows(windows):
    517    return [calculate_peak_height(window) for window in windows]

def get_cadence_for_windows(windows, fs): View Source

    519def get_cadence_for_windows(windows, fs):
    520    return [calculate_cadence(window, fs) for window in windows]

def get_freezing_index_for_windows(windows, fs): View Source

    522def get_freezing_index_for_windows(windows, fs):
    523    return [calculate_freezing_index(window, fs) for window in windows]

def get_dominant_frequency_for_windows(windows, fs): View Source

    525def get_dominant_frequency_for_windows(windows, fs):
    526    return [calculate_dominant_frequency(window, fs) for window in windows]

def get_peak_frequency_for_windows(windows, fs): View Source

    528def get_peak_frequency_for_windows(windows, fs):
    529    return [calculate_peak_frequency(window, fs) for window in windows]

def get_power_spectral_entropy_for_windows(windows, fs): View Source

    531def get_power_spectral_entropy_for_windows(windows, fs):
    532    return [calculate_power_spectral_entropy(window, fs) for window in windows]

def get_principal_harmonic_frequency_for_windows(windows, fs): View Source

    534def get_principal_harmonic_frequency_for_windows(windows, fs):
    535    return [calculate_principal_harmonic_frequency(window, fs) for window in windows]

def get_entropy_for_windows(windows): View Source

    537def get_entropy_for_windows(windows):
    538    return [calculate_entropy(window) for window in windows]

def get_interquartile_range_for_windows(windows): View Source

    540def get_interquartile_range_for_windows(windows):
    541    return [calculate_interquartile_range(window) for window in windows]

def get_correlation_for_windows(windows): View Source

    543def get_correlation_for_windows(windows):
    544    # For correlation, we need to handle it differently since it needs two signals
    545    # We'll calculate autocorrelation for each window
    546    return [calculate_correlation(window[:-1], window[1:]) if len(window) > 1 else 0 for window in windows]

def get_auto_regression_coefficients_for_windows(windows, order=3): View Source

    548def get_auto_regression_coefficients_for_windows(windows, order=3):
    549    return [calculate_auto_regression_coefficients(window, order) for window in windows]

def extract_gait_features( daphnet_windows, fs, time_domain=True, frequency_domain=True, statistical=True, verbose=True): View Source

    551def extract_gait_features(daphnet_windows, fs, time_domain=True, frequency_domain=True, statistical=True, verbose=True):
    552    """
    553    Legacy function for extracting gait features.
    554
    555    Args:
    556        daphnet_windows: List of sliding window dictionaries
    557        fs: Sampling frequency
    558        time_domain: Whether to extract time domain features
    559        frequency_domain: Whether to extract frequency domain features
    560        statistical: Whether to extract statistical features
    561        verbose: Whether to show verbose output and progress bars
    562
    563    Returns:
    564        List of feature dictionaries
    565    """
    566    extractor = GaitFeatureExtractor(verbose=verbose)
    567    return extractor.extract_features(
    568        daphnet_windows, fs,
    569        time_domain=time_domain,
    570        frequency_domain=frequency_domain,
    571        statistical=statistical
    572    )

Legacy function for extracting gait features.

Args: daphnet_windows: List of sliding window dictionaries fs: Sampling frequency time_domain: Whether to extract time domain features frequency_domain: Whether to extract frequency domain features statistical: Whether to extract statistical features verbose: Whether to show verbose output and progress bars

Returns: List of feature dictionaries