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Refactored Gait configuration files and improved constraints
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@vedran-kasalica
vedran-kasalica committed Sep 5, 2024
1 parent ac39234 commit d44b3af
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Showing 4 changed files with 166 additions and 167 deletions.
60 changes: 36 additions & 24 deletions src/paradigma/constants.py
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Expand Up @@ -4,47 +4,59 @@
@dataclass(frozen=True)
class DataColumns():
"""
Enum for the data channels in tsdf.
Class containing the data channels in `tsdf`.
"""
ACCELEROMETER_X = "accelerometer_x"
ACCELEROMETER_Y = "accelerometer_y"
ACCELEROMETER_Z = "accelerometer_z"
GYROSCOPE_X = "gyroscope_x"
GYROSCOPE_Y = "gyroscope_y"
GYROSCOPE_Z = "gyroscope_z"
PPG = "green"
TIME = "time"
ACCELEROMETER_X : str = "accelerometer_x"
ACCELEROMETER_Y : str = "accelerometer_y"
ACCELEROMETER_Z : str = "accelerometer_z"
GYROSCOPE_X : str = "gyroscope_x"
GYROSCOPE_Y : str = "gyroscope_y"
GYROSCOPE_Z : str = "gyroscope_z"
PPG : str = "green"
TIME : str = "time"

# The following are used in gait analysis
GRAV_ACCELEROMETER_X = "grav_accelerometer_x"
GRAV_ACCELEROMETER_Y = "grav_accelerometer_y"
GRAV_ACCELEROMETER_Z = "grav_accelerometer_z"
PRED_GAIT = "pred_gait"
ANGLE = "angle"
ANGLE_SMOOTH = "angle_smooth"
VELOCITY = "velocity"
SEGMENT_NR = "segment_nr"
GRAV_ACCELEROMETER_X : str = "grav_accelerometer_x"
GRAV_ACCELEROMETER_Y : str = "grav_accelerometer_y"
GRAV_ACCELEROMETER_Z : str = "grav_accelerometer_z"
PRED_GAIT : str = "pred_gait"
PRED_ARM_SWING : str = "pred_arm_swing"
ANGLE : str = "angle"
ANGLE_SMOOTH : str = "angle_smooth"
VELOCITY : str = "velocity"
SEGMENT_NR : str = "segment_nr"

@dataclass(frozen=True)
class DataUnits():
"""
Enum for the data channel unit types in tsdf.
Class containing the data channel unit types in `tsdf`.
"""
ACCELERATION = "m/s^2"
ACCELERATION: str = "m/s^2"
""" The acceleration is in m/s^2. """
ROTATION = "deg/s"

ROTATION: str = "deg/s"
""" The rotation is in degrees per second. """

GRAVITY: str = "g"
""" The acceleration due to gravity is in g. """

POWER_SPECTRAL_DENSITY: str = "g^2/Hz"
""" The power spectral density is in g^2/Hz. """

FREQUENCY: str = "Hz"
""" The frequency is in Hz. """


@dataclass(frozen=True)
class TimeUnit():
"""
Enum for the `time` channel unit types in tsdf.
Class containing the `time` channel unit types in `tsdf`.
"""
RELATIVE_MS = "relative_ms"
RELATIVE_MS : str = "relative_ms"
""" The time is relative to the start time in milliseconds. """
ABSOLUTE_MS = "absolute_ms"
ABSOLUTE_MS : str = "absolute_ms"
""" The time is absolute in milliseconds. """
DIFFERENCE_MS = "difference_ms"
DIFFERENCE_MS : str = "difference_ms"
""" The time is the difference between consecutive samples in milliseconds. """

UNIX_TICKS_MS: int = 1000
4 changes: 2 additions & 2 deletions src/paradigma/feature_extraction.py
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Expand Up @@ -8,7 +8,7 @@
from scipy.signal import find_peaks

from paradigma.constants import DataColumns
from paradigma.gait_analysis_config import GaitFeatureExtractionConfig, ArmSwingFeatureExtractionConfig
from paradigma.gait_analysis_config import IMUConfig


def generate_statistics(
Expand Down Expand Up @@ -606,7 +606,7 @@ def extract_peak_angular_velocity(
return


def extract_temporal_domain_features(config:GaitFeatureExtractionConfig | ArmSwingFeatureExtractionConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame:
def extract_temporal_domain_features(config: IMUConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame:
"""
Compute temporal domain features for the accelerometer signal. The features are added to the dataframe. Therefore the original dataframe is modified, and the modified dataframe is returned.
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24 changes: 12 additions & 12 deletions src/paradigma/gait_analysis.py
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Expand Up @@ -71,12 +71,12 @@ def detect_gait(input_path: Union[str, Path], output_path: Union[str, Path], pat
threshold = float(f.read())

# Prepare the data
clf.feature_names_in_ = [f'{x}_power_below_gait' for x in config.l_accel_cols] + \
[f'{x}_power_gait' for x in config.l_accel_cols] + \
[f'{x}_power_tremor' for x in config.l_accel_cols] + \
[f'{x}_power_above_tremor' for x in config.l_accel_cols] + \
['std_norm_acc'] + [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'grav_{x}_{y}' for x in config.l_accel_cols for y in ['mean', 'std']] + \
[f'{x}_dominant_frequency' for x in config.l_accel_cols]
clf.feature_names_in_ = [f'{x}_power_below_gait' for x in config.l_accelerometer_cols] + \
[f'{x}_power_gait' for x in config.l_accelerometer_cols] + \
[f'{x}_power_tremor' for x in config.l_accelerometer_cols] + \
[f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \
['std_norm_acc'] + [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'grav_{x}_{y}' for x in config.l_accelerometer_cols for y in ['mean', 'std']] + \
[f'{x}_dominant_frequency' for x in config.l_accelerometer_cols]
X = df.loc[:, clf.feature_names_in_]

# Make prediction
Expand Down Expand Up @@ -292,15 +292,15 @@ def detect_arm_swing(input_path: Union[str, Path], output_path: Union[str, Path]
clf = pd.read_pickle(os.path.join(path_to_classifier_input, config.classifier_file_name))

# Prepare the data
clf.feature_names_in_ = ['std_norm_acc'] + [f'{x}_power_below_gait' for x in config.l_accel_cols] + \
[f'{x}_power_gait' for x in config.l_accel_cols] + \
[f'{x}_power_tremor' for x in config.l_accel_cols] + \
[f'{x}_power_above_tremor' for x in config.l_accel_cols] + \
clf.feature_names_in_ = ['std_norm_acc'] + [f'{x}_power_below_gait' for x in config.l_accelerometer_cols] + \
[f'{x}_power_gait' for x in config.l_accelerometer_cols] + \
[f'{x}_power_tremor' for x in config.l_accelerometer_cols] + \
[f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \
[f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'cc_{i}_gyroscope' for i in range(1, 13)] + \
[f'grav_{x}_mean' for x in config.l_accel_cols] + [f'grav_{x}_std' for x in config.l_accel_cols] + \
[f'grav_{x}_mean' for x in config.l_accelerometer_cols] + [f'grav_{x}_std' for x in config.l_accelerometer_cols] + \
['range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean', 'forward_peak_ang_vel_std',
'backward_peak_ang_vel_std', 'angle_perc_power', 'angle_dominant_frequency'] + \
[f'{x}_dominant_frequency' for x in config.l_accel_cols]
[f'{x}_dominant_frequency' for x in config.l_accelerometer_cols]

X = df.loc[:, clf.feature_names_in_]

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