Gait Sequence Detection

Gait Sequence Detection (Paraschiv-Ionescu)

The Paraschiv-Ionescu gait sequence detection algorithm identifies gait sequences in accelerometer data from a lower back sensor.

The algorithm detects gait sequences based on identified steps. It starts by loading the accelerometer data, which includes three columns corresponding to the acceleration signals across the x, y, and z axes, along with the sampling frequency of the data. To simplify the analysis, the norm of acceleration is computed. Next, the signal is resampled at a 40 Hz sampling frequency using interpolation. Smoothing is then applied through a Savitzky-Golay filter and a Finite Impulse Response (FIR) low-pass filter to remove noise and drifts from the signal. The continuous wavelet transform is applied to capture gait-related features, followed by additional smoothing using successive Gaussian-weighted filters. The processed data is then analyzed to detect gait sequences.

The algorithm continues by identifying the envelope of the processed acceleration signal. Active periods of the signal are identified using the Hilbert envelope. The statistical distribution of the amplitude of the peaks in these active periods is used to derive an adaptive threshold. In case the Hilbert envelope algorithm fails to detect active periods, a fixed threshold value (0.15 g) is used for peak detection in the signal. Mid-swing peaks are detected based on this threshold. Pulse trains in the local maximum and minimum of the peaks are identified, with those having fewer than four steps filtered out. The intersection of pulse trains from local maximum and minimum peaks is detected as walking periods. These periods are then organized and grouped to update the start and end times of detected walking bouts.

Next, the algorithm takes the last steps to detect walking bouts in the signal. For this purpose, walking bouts with five or more steps are detected, and their start and end times are added to the list. Walking labels are generated as an array of zeros, and the intervals corresponding to the walking bouts are labeled as 1. Groups of consecutive zeros in the walking labels are identified, and if breaks between walking bouts are less than three seconds, they are merged. If gait sequences are found, the output is printed; otherwise, a message indicating that no gait sequences are detected is displayed.

Finally, the gait sequence information is stored in the 'gait_sequences_' attribute in BIDS compatible format with columns onset, duration, event_type, tracking_system as Pandas DataFrame.

Methods:

Name	Description
detect	Detects gait sequences in the provided accelerometer data.

Examples:

>>> gsd = ParaschivIonescuGaitSequenceDetection()
>>> gsd.detect(accel_data=acceleration_data, sampling_freq_Hz=100, plot_results=True)
>>> print(gsd.gait_sequences_)
        onset   duration    event_type      tracking_systems
    0   4.500   5.25        gait sequence   SU
    1   90.225  10.30       gait sequence   SU

References

[1] Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer...

[2] Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU...

Source code in kielmat/modules/gsd/_paraschiv.py

class ParaschivIonescuGaitSequenceDetection:
    """
    The Paraschiv-Ionescu gait sequence detection algorithm identifies gait sequences in accelerometer data from a lower back sensor.

    The algorithm detects gait sequences based on identified steps. It starts by loading the accelerometer data, which includes three
    columns corresponding to the acceleration signals across the x, y, and z axes, along with the sampling frequency of the data. To
    simplify the analysis, the norm of acceleration is computed. Next, the signal is resampled at a 40 Hz sampling frequency using
    interpolation. Smoothing is then applied through a Savitzky-Golay filter and a Finite Impulse Response (FIR) low-pass filter to
    remove noise and drifts from the signal. The continuous wavelet transform is applied to capture gait-related features, followed by
    additional smoothing using successive Gaussian-weighted filters. The processed data is then analyzed to detect gait sequences.

    The algorithm continues by identifying the envelope of the processed acceleration signal. Active periods of the signal are identified
    using the Hilbert envelope. The statistical distribution of the amplitude of the peaks in these active periods is used to derive an
    adaptive threshold. In case the Hilbert envelope algorithm fails to detect active periods, a fixed threshold value (0.15 g) is used
    for peak detection in the signal. Mid-swing peaks are detected based on this threshold. Pulse trains in the local maximum and minimum
    of the peaks are identified, with those having fewer than four steps filtered out. The intersection of pulse trains from local maximum
    and minimum peaks is detected as walking periods. These periods are then organized and grouped to update the start and end times of
    detected walking bouts.

    Next, the algorithm takes the last steps to detect walking bouts in the signal. For this purpose, walking bouts with five or more steps
    are detected, and their start and end times are added to the list. Walking labels are generated as an array of zeros, and the intervals
    corresponding to the walking bouts are labeled as 1. Groups of consecutive zeros in the walking labels are identified, and if breaks
    between walking bouts are less than three seconds, they are merged. If gait sequences are found, the output is printed; otherwise, a
    message indicating that no gait sequences are detected is displayed.

    Finally, the gait sequence information is stored in the 'gait_sequences_' attribute in BIDS compatible format with columns `onset`,
    `duration`, `event_type`, `tracking_system` as Pandas DataFrame.

    Methods:
        detect(accel_data, sampling_freq_Hz, plot_results=False):
            Detects gait sequences in the provided accelerometer data.

    Examples:
        >>> gsd = ParaschivIonescuGaitSequenceDetection()
        >>> gsd.detect(accel_data=acceleration_data, sampling_freq_Hz=100, plot_results=True)
        >>> print(gsd.gait_sequences_)
                onset   duration    event_type      tracking_systems
            0   4.500   5.25        gait sequence   SU
            1   90.225  10.30       gait sequence   SU

    References:
        [1] Paraschiv-Ionescu et al. (2019). Locomotion and cadence detection using a single trunk-fixed accelerometer...

        [2] Paraschiv-Ionescu et al. (2020). Real-world speed estimation using single trunk IMU...
    """

    def __init__(
        self,
    ):
        """
        Initializes the ParaschivIonescuGaitSequenceDetection instance.
        """
        self.gait_sequences_ = None

    def detect(
        self,
        accel_data: pd.DataFrame,
        sampling_freq_Hz: float,
        plot_results: bool = False,
        dt_data: Optional[pd.Series] = None,
        tracking_system: Optional[str] = None,
    ) -> pd.DataFrame:
        """
        Detects gait sequences based on the input accelerometer data.

        Args:
            accel_data (pd.DataFrame): Input accelerometer data (N, 3) for x, y, and z axes.
            sampling_freq_Hz (float): Sampling frequency of the accelerometer data.
            plot_results (bool, optional): If True, generates a plot showing the pre-processed acceleration data
                and the detected gait sequences. Default is False.
            dt_data (pd.Series, optional): Original datetime in the input data. If original datetime is provided, the output onset will be based on that.
            tracking_system (str, optional): Tracking system the data is from to be used for events df. Default is None.

        Returns:
            pd.DataFrame: The gait sequence information stored in the 'gait_sequences_' attribute,
                which is a pandas DataFrame in BIDS format with the following columns:
                    - onset: Start time of the gait sequence.
                    - duration: Duration of the gait sequence.
                    - event_type: Type of the event (default is 'gait sequence').
                    - tracking_system: Tracking systems used the events are derived from.
        """
        # Error handling for invalid input data
        if not isinstance(accel_data, pd.DataFrame) or accel_data.shape[1] != 3:
            raise ValueError(
                "Input accelerometer data must be a DataFrame with 3 columns for x, y, and z axes."
            )

        if not isinstance(sampling_freq_Hz, (int, float)) or sampling_freq_Hz <= 0:
            raise ValueError("Sampling frequency must be a positive float.")

        if not isinstance(plot_results, bool):
            raise ValueError("Plot results must be a boolean (True or False).")

        # check if tracking_system is a string
        if tracking_system is not None and not isinstance(tracking_system, str):
            raise ValueError("tracking_system must be a string")

        # check if dt_data is a pandas Series with datetime values
        if dt_data is not None and (
            not isinstance(dt_data, pd.Series)
            or not pd.api.types.is_datetime64_any_dtype(dt_data)
        ):
            raise ValueError("dt_data must be a pandas Series with datetime values")

        # check if dt_data is provided and if it is a series with the same length as data
        if dt_data is not None and len(dt_data) != len(accel_data):
            raise ValueError("dt_data must be a series with the same length as data")

        # Convert acceleration data from "m/s^2" to "g"
        accel_data /= 9.81

        # Calculate the norm of acceleration
        acceleration_norm = np.linalg.norm(accel_data, axis=1)

        # Resample acceleration_norm to target sampling frequency
        initial_sampling_frequency = sampling_freq_Hz
        target_sampling_freq_Hz = 40
        resampled_acceleration = preprocessing.resample_interpolate(
            acceleration_norm, initial_sampling_frequency, target_sampling_freq_Hz
        )

        # Applying low-pass Savitzky-Golay filter to smoothen the resampled data
        smoothed_acceleration = preprocessing.lowpass_filter(
            resampled_acceleration,
            method="savgol",
            window_length=21,
            polynomial_order=7,
        )

        # Remove 40Hz drift from the filtered data
        drift_removed_acceleration = preprocessing.highpass_filter(
            signal=smoothed_acceleration,
            sampling_frequency=target_sampling_freq_Hz,
            method="iir",
        )

        # Filter data using the fir low-pass filter
        filtered_acceleration = preprocessing.lowpass_filter(
            drift_removed_acceleration, method="fir"
        )

        # Perform the continuous wavelet transform on the filtered acceleration data
        wavelet_transform_result = preprocessing.apply_continuous_wavelet_transform(
            filtered_acceleration,
            scales=10,
            desired_scale=10,
            wavelet="gaus2",
            sampling_frequency=target_sampling_freq_Hz,
        )

        # Applying Savitzky-Golay filter to further smoothen the wavelet transformed data
        smoothed_wavelet_result = preprocessing.lowpass_filter(
            wavelet_transform_result, window_length=11, polynomial_order=5
        )

        # Perform continuous wavelet transform
        further_smoothed_wavelet_result = (
            preprocessing.apply_continuous_wavelet_transform(
                smoothed_wavelet_result,
                scales=10,
                desired_scale=10,
                wavelet="gaus2",
                sampling_frequency=target_sampling_freq_Hz,
            )
        )
        further_smoothed_wavelet_result = further_smoothed_wavelet_result.T

        # Smoothing the data using successive Gaussian filters
        filtered_signal = preprocessing.apply_successive_gaussian_filters(
            further_smoothed_wavelet_result
        )

        # Use pre-processsed signal for post-processing purposes
        detected_activity_signal = filtered_signal

        # Compute the envelope of the processed acceleration data
        envelope, _ = preprocessing.calculate_envelope_activity(
            detected_activity_signal,
            int(round(target_sampling_freq_Hz)),
            1,
            int(round(target_sampling_freq_Hz)),
        )

        # Initialize a list for walking bouts
        walking_bouts = [0]

        # Process alarm data to identify walking bouts
        if envelope.size > 0:
            index_ranges = preprocessing.find_consecutive_groups(envelope > 0)
            for j in range(len(index_ranges)):
                if (
                    index_ranges[j, 1] - index_ranges[j, 0]
                    <= 3 * target_sampling_freq_Hz
                ):
                    envelope[index_ranges[j, 0] : index_ranges[j, 1] + 1] = 0
                else:
                    walking_bouts.extend(
                        detected_activity_signal[
                            index_ranges[j, 0] : index_ranges[j, 1] + 1
                        ]
                    )

            # Convert walk_low_back list to a NumPy array
            walking_bouts_array = np.array(walking_bouts)

            # Find positive peaks in the walk_low_back_array
            positive_peak_indices, _ = scipy.signal.find_peaks(walking_bouts_array)

            # Get the corresponding y-axis data values for the positive peak
            positive_peaks = walking_bouts_array[positive_peak_indices]

            # Find negative peaks in the inverted walk_low_back array
            negative_peak_indices, _ = scipy.signal.find_peaks(-walking_bouts_array)

            # Get the corresponding y-axis data values for the positive peak
            negative_peaks = -walking_bouts_array[negative_peak_indices]

            # Combine positive and negative peaks
            combined_peaks = [x for x in positive_peaks if x > 0] + [
                x for x in negative_peaks if x > 0
            ]

            # Calculate the data adaptive threshold using the 5th percentile of the combined peaks
            try:
                threshold = np.percentile(combined_peaks, 5)

            except IndexError:
                # If combined_peaks is empty, set threshold to default value
                threshold = 0.15
                selected_signal = smoothed_wavelet_result

            # Set selected_signal to detected_activity_signal
            selected_signal = detected_activity_signal

        # Detect mid-swing peaks
        min_peaks, max_peaks = preprocessing.find_local_min_max(
            selected_signal, threshold
        )

        # Find pulse trains in max_peaks and remove ones with steps less than 4
        pulse_trains_max = preprocessing.identify_pulse_trains(max_peaks)

        # Access the fields of the struct-like array
        pulse_trains_max = [train for train in pulse_trains_max if train["steps"] >= 4]

        # Find pulse trains in min_peaks and remove ones with steps less than 4
        pulse_trains_min = preprocessing.identify_pulse_trains(min_peaks)

        # Access the fields of the struct-like array
        pulse_trains_min = [train for train in pulse_trains_min if train["steps"] >= 4]

        # Convert t1 and t2 to sets and find their intersection
        walking_periods = preprocessing.find_interval_intersection(
            preprocessing.convert_pulse_train_to_array(pulse_trains_max),
            preprocessing.convert_pulse_train_to_array(pulse_trains_min),
        )

        # Check if walking_periods is empty
        if walking_periods is None:
            walking_bouts = []

        else:
            # Call the organize_and_pack_results function with walking_periods and MaxPeaks
            walking_bouts, _ = preprocessing.organize_and_pack_results(
                walking_periods, max_peaks
            )
            if walking_bouts:
                # Update the start value of the first element
                walking_bouts[0]["start"] = max([1, walking_bouts[0]["start"]])

                # Update the end value of the last element
                walking_bouts[-1]["end"] = min(
                    [walking_bouts[-1]["end"], len(detected_activity_signal)]
                )

        # Calculate the length of walking bouts
        walking_bouts_length = len(walking_bouts)

        # Initialize an empty list for filtered walking bouts
        filtered_walking_bouts = []

        # Initialize a counter variable to count walking bouts
        counter = 0

        # Iterate through walking bouts to filter those with steps less than 5
        for j in range(walking_bouts_length):
            if walking_bouts[j]["steps"] >= 5:
                counter += 1
                filtered_walking_bouts.append(
                    {"start": walking_bouts[j]["start"], "end": walking_bouts[j]["end"]}
                )

        # If no walking bouts are detected, print a message
        if counter == 0:
            print("No gait sequences detected due to insufficient steps in the data.")
            return self

        # Initialize an array of zeros with the length of detected_activity_signal
        walking_labels = np.zeros(len(detected_activity_signal))

        # Calculate the length of the filtered_walking_bouts
        filtered_walking_bouts_length = len(filtered_walking_bouts)

        for j in range(filtered_walking_bouts_length):
            walking_labels[
                filtered_walking_bouts[j]["start"] : filtered_walking_bouts[j]["end"]
                + 1
            ] = 1

        # Call the find_consecutive_groups function with the walking_labels variable
        ind_noWk = []
        ind_noWk = preprocessing.find_consecutive_groups(walking_labels == 0)

        # Merge walking bouts if break less than 3 seconds
        if ind_noWk.size > 0:
            for j in range(len(ind_noWk)):
                if ind_noWk[j, 1] - ind_noWk[j, 0] <= target_sampling_freq_Hz * 3:
                    walking_labels[ind_noWk[j, 0] : ind_noWk[j, 1] + 1] = 1

        # Merge walking bouts if break less than 3 seconds
        ind_Wk = []
        walkLabel_1_indices = np.where(walking_labels == 1)[0]
        GSD_Output = []

        if walkLabel_1_indices.size > 0:
            ind_Wk = preprocessing.find_consecutive_groups(walking_labels == 1)
            # Create an empty list to store 'walk' dictionaries
            walk = []
            if ind_Wk.size > 0:
                for j in range(len(ind_Wk)):
                    walk.append({"start": (ind_Wk[j, 0]), "end": ind_Wk[j, 1]})

            n = len(walk)

            for j in range(n):
                GSD_Output.append(
                    {
                        "Start": walk[j]["start"] / target_sampling_freq_Hz,
                        "End": walk[j]["end"] / target_sampling_freq_Hz,
                        "fs": sampling_freq_Hz,
                    }
                )
            print(f"{n} gait sequence(s) detected.")
        else:
            print("No gait sequence(s) detected.")

        # Create a DataFrame from the gait sequence data
        gait_sequences_ = pd.DataFrame(GSD_Output)
        gait_sequences_["onset"] = gait_sequences_["Start"]
        gait_sequences_["duration"] = gait_sequences_["End"] - gait_sequences_["Start"]
        gait_sequences_["event_type"] = "gait sequence"
        gait_sequences_["tracking_system"] = tracking_system

        # Check if the indices in ind_Wk are within the range of dt_data's index
        if ind_Wk.size > 0 and dt_data is not None:
            valid_indices = [index for index in ind_Wk[:, 0] if index < len(dt_data)]
            invalid_indices = len(ind_Wk[:, 0]) - len(valid_indices)

            if invalid_indices > 0:
                print(f"Warning: {invalid_indices} invalid index/indices found.")

            # Only use valid indices to access dt_data
            valid_dt_data = dt_data.iloc[valid_indices]

            # Create a DataFrame from the gait sequence data
            gait_sequences_ = pd.DataFrame(GSD_Output)
            gait_sequences_["onset"] = gait_sequences_["Start"]
            gait_sequences_["duration"] = (
                gait_sequences_["End"] - gait_sequences_["Start"]
            )
            gait_sequences_["event_type"] = "gait sequence"
            gait_sequences_["tracking_system"] = tracking_system

            # If original datetime is available, update the 'onset' column
            gait_sequences_["onset"] = valid_dt_data.reset_index(drop=True)

        # Create a DataFrame from the gait sequence data
        gait_sequences_ = gait_sequences_[
            ["onset", "duration", "event_type", "tracking_system"]
        ]

        # Return gait_sequences_ as an output
        self.gait_sequences_ = gait_sequences_

        # If Plot_results set to true
        # currently no plotting for datetime values
        if dt_data is not None and plot_results:
            print("No plotting for datetime values.")
            plot_results = False
            return self

        # Plot results if set to true
        if plot_results:
            # Convert detected_activity_signal from g back to m/s^2 for consistency
            detected_activity_signal *= 9.81

            viz_utils.plot_gait(
                target_sampling_freq_Hz, detected_activity_signal, gait_sequences_
            )

        return self

__init__()

Initializes the ParaschivIonescuGaitSequenceDetection instance.

Source code in kielmat/modules/gsd/_paraschiv.py

def __init__(
    self,
):
    """
    Initializes the ParaschivIonescuGaitSequenceDetection instance.
    """
    self.gait_sequences_ = None

detect(accel_data, sampling_freq_Hz, plot_results=False, dt_data=None, tracking_system=None)

Detects gait sequences based on the input accelerometer data.

Parameters:

Name	Type	Description	Default
accel_data	DataFrame	Input accelerometer data (N, 3) for x, y, and z axes.	required
sampling_freq_Hz	float	Sampling frequency of the accelerometer data.	required
plot_results	bool	If True, generates a plot showing the pre-processed acceleration data and the detected gait sequences. Default is False.	False
dt_data	Series	Original datetime in the input data. If original datetime is provided, the output onset will be based on that.	None
tracking_system	str	Tracking system the data is from to be used for events df. Default is None.	None

Returns:

Type	Description
DataFrame	pd.DataFrame: The gait sequence information stored in the 'gait_sequences_' attribute, which is a pandas DataFrame in BIDS format with the following columns: - onset: Start time of the gait sequence. - duration: Duration of the gait sequence. - event_type: Type of the event (default is 'gait sequence'). - tracking_system: Tracking systems used the events are derived from.

Source code in kielmat/modules/gsd/_paraschiv.py

def detect(
    self,
    accel_data: pd.DataFrame,
    sampling_freq_Hz: float,
    plot_results: bool = False,
    dt_data: Optional[pd.Series] = None,
    tracking_system: Optional[str] = None,
) -> pd.DataFrame:
    """
    Detects gait sequences based on the input accelerometer data.

    Args:
        accel_data (pd.DataFrame): Input accelerometer data (N, 3) for x, y, and z axes.
        sampling_freq_Hz (float): Sampling frequency of the accelerometer data.
        plot_results (bool, optional): If True, generates a plot showing the pre-processed acceleration data
            and the detected gait sequences. Default is False.
        dt_data (pd.Series, optional): Original datetime in the input data. If original datetime is provided, the output onset will be based on that.
        tracking_system (str, optional): Tracking system the data is from to be used for events df. Default is None.

    Returns:
        pd.DataFrame: The gait sequence information stored in the 'gait_sequences_' attribute,
            which is a pandas DataFrame in BIDS format with the following columns:
                - onset: Start time of the gait sequence.
                - duration: Duration of the gait sequence.
                - event_type: Type of the event (default is 'gait sequence').
                - tracking_system: Tracking systems used the events are derived from.
    """
    # Error handling for invalid input data
    if not isinstance(accel_data, pd.DataFrame) or accel_data.shape[1] != 3:
        raise ValueError(
            "Input accelerometer data must be a DataFrame with 3 columns for x, y, and z axes."
        )

    if not isinstance(sampling_freq_Hz, (int, float)) or sampling_freq_Hz <= 0:
        raise ValueError("Sampling frequency must be a positive float.")

    if not isinstance(plot_results, bool):
        raise ValueError("Plot results must be a boolean (True or False).")

    # check if tracking_system is a string
    if tracking_system is not None and not isinstance(tracking_system, str):
        raise ValueError("tracking_system must be a string")

    # check if dt_data is a pandas Series with datetime values
    if dt_data is not None and (
        not isinstance(dt_data, pd.Series)
        or not pd.api.types.is_datetime64_any_dtype(dt_data)
    ):
        raise ValueError("dt_data must be a pandas Series with datetime values")

    # check if dt_data is provided and if it is a series with the same length as data
    if dt_data is not None and len(dt_data) != len(accel_data):
        raise ValueError("dt_data must be a series with the same length as data")

    # Convert acceleration data from "m/s^2" to "g"
    accel_data /= 9.81

    # Calculate the norm of acceleration
    acceleration_norm = np.linalg.norm(accel_data, axis=1)

    # Resample acceleration_norm to target sampling frequency
    initial_sampling_frequency = sampling_freq_Hz
    target_sampling_freq_Hz = 40
    resampled_acceleration = preprocessing.resample_interpolate(
        acceleration_norm, initial_sampling_frequency, target_sampling_freq_Hz
    )

    # Applying low-pass Savitzky-Golay filter to smoothen the resampled data
    smoothed_acceleration = preprocessing.lowpass_filter(
        resampled_acceleration,
        method="savgol",
        window_length=21,
        polynomial_order=7,
    )

    # Remove 40Hz drift from the filtered data
    drift_removed_acceleration = preprocessing.highpass_filter(
        signal=smoothed_acceleration,
        sampling_frequency=target_sampling_freq_Hz,
        method="iir",
    )

    # Filter data using the fir low-pass filter
    filtered_acceleration = preprocessing.lowpass_filter(
        drift_removed_acceleration, method="fir"
    )

    # Perform the continuous wavelet transform on the filtered acceleration data
    wavelet_transform_result = preprocessing.apply_continuous_wavelet_transform(
        filtered_acceleration,
        scales=10,
        desired_scale=10,
        wavelet="gaus2",
        sampling_frequency=target_sampling_freq_Hz,
    )

    # Applying Savitzky-Golay filter to further smoothen the wavelet transformed data
    smoothed_wavelet_result = preprocessing.lowpass_filter(
        wavelet_transform_result, window_length=11, polynomial_order=5
    )

    # Perform continuous wavelet transform
    further_smoothed_wavelet_result = (
        preprocessing.apply_continuous_wavelet_transform(
            smoothed_wavelet_result,
            scales=10,
            desired_scale=10,
            wavelet="gaus2",
            sampling_frequency=target_sampling_freq_Hz,
        )
    )
    further_smoothed_wavelet_result = further_smoothed_wavelet_result.T

    # Smoothing the data using successive Gaussian filters
    filtered_signal = preprocessing.apply_successive_gaussian_filters(
        further_smoothed_wavelet_result
    )

    # Use pre-processsed signal for post-processing purposes
    detected_activity_signal = filtered_signal

    # Compute the envelope of the processed acceleration data
    envelope, _ = preprocessing.calculate_envelope_activity(
        detected_activity_signal,
        int(round(target_sampling_freq_Hz)),
        1,
        int(round(target_sampling_freq_Hz)),
    )

    # Initialize a list for walking bouts
    walking_bouts = [0]

    # Process alarm data to identify walking bouts
    if envelope.size > 0:
        index_ranges = preprocessing.find_consecutive_groups(envelope > 0)
        for j in range(len(index_ranges)):
            if (
                index_ranges[j, 1] - index_ranges[j, 0]
                <= 3 * target_sampling_freq_Hz
            ):
                envelope[index_ranges[j, 0] : index_ranges[j, 1] + 1] = 0
            else:
                walking_bouts.extend(
                    detected_activity_signal[
                        index_ranges[j, 0] : index_ranges[j, 1] + 1
                    ]
                )

        # Convert walk_low_back list to a NumPy array
        walking_bouts_array = np.array(walking_bouts)

        # Find positive peaks in the walk_low_back_array
        positive_peak_indices, _ = scipy.signal.find_peaks(walking_bouts_array)

        # Get the corresponding y-axis data values for the positive peak
        positive_peaks = walking_bouts_array[positive_peak_indices]

        # Find negative peaks in the inverted walk_low_back array
        negative_peak_indices, _ = scipy.signal.find_peaks(-walking_bouts_array)

        # Get the corresponding y-axis data values for the positive peak
        negative_peaks = -walking_bouts_array[negative_peak_indices]

        # Combine positive and negative peaks
        combined_peaks = [x for x in positive_peaks if x > 0] + [
            x for x in negative_peaks if x > 0
        ]

        # Calculate the data adaptive threshold using the 5th percentile of the combined peaks
        try:
            threshold = np.percentile(combined_peaks, 5)

        except IndexError:
            # If combined_peaks is empty, set threshold to default value
            threshold = 0.15
            selected_signal = smoothed_wavelet_result

        # Set selected_signal to detected_activity_signal
        selected_signal = detected_activity_signal

    # Detect mid-swing peaks
    min_peaks, max_peaks = preprocessing.find_local_min_max(
        selected_signal, threshold
    )

    # Find pulse trains in max_peaks and remove ones with steps less than 4
    pulse_trains_max = preprocessing.identify_pulse_trains(max_peaks)

    # Access the fields of the struct-like array
    pulse_trains_max = [train for train in pulse_trains_max if train["steps"] >= 4]

    # Find pulse trains in min_peaks and remove ones with steps less than 4
    pulse_trains_min = preprocessing.identify_pulse_trains(min_peaks)

    # Access the fields of the struct-like array
    pulse_trains_min = [train for train in pulse_trains_min if train["steps"] >= 4]

    # Convert t1 and t2 to sets and find their intersection
    walking_periods = preprocessing.find_interval_intersection(
        preprocessing.convert_pulse_train_to_array(pulse_trains_max),
        preprocessing.convert_pulse_train_to_array(pulse_trains_min),
    )

    # Check if walking_periods is empty
    if walking_periods is None:
        walking_bouts = []

    else:
        # Call the organize_and_pack_results function with walking_periods and MaxPeaks
        walking_bouts, _ = preprocessing.organize_and_pack_results(
            walking_periods, max_peaks
        )
        if walking_bouts:
            # Update the start value of the first element
            walking_bouts[0]["start"] = max([1, walking_bouts[0]["start"]])

            # Update the end value of the last element
            walking_bouts[-1]["end"] = min(
                [walking_bouts[-1]["end"], len(detected_activity_signal)]
            )

    # Calculate the length of walking bouts
    walking_bouts_length = len(walking_bouts)

    # Initialize an empty list for filtered walking bouts
    filtered_walking_bouts = []

    # Initialize a counter variable to count walking bouts
    counter = 0

    # Iterate through walking bouts to filter those with steps less than 5
    for j in range(walking_bouts_length):
        if walking_bouts[j]["steps"] >= 5:
            counter += 1
            filtered_walking_bouts.append(
                {"start": walking_bouts[j]["start"], "end": walking_bouts[j]["end"]}
            )

    # If no walking bouts are detected, print a message
    if counter == 0:
        print("No gait sequences detected due to insufficient steps in the data.")
        return self

    # Initialize an array of zeros with the length of detected_activity_signal
    walking_labels = np.zeros(len(detected_activity_signal))

    # Calculate the length of the filtered_walking_bouts
    filtered_walking_bouts_length = len(filtered_walking_bouts)

    for j in range(filtered_walking_bouts_length):
        walking_labels[
            filtered_walking_bouts[j]["start"] : filtered_walking_bouts[j]["end"]
            + 1
        ] = 1

    # Call the find_consecutive_groups function with the walking_labels variable
    ind_noWk = []
    ind_noWk = preprocessing.find_consecutive_groups(walking_labels == 0)

    # Merge walking bouts if break less than 3 seconds
    if ind_noWk.size > 0:
        for j in range(len(ind_noWk)):
            if ind_noWk[j, 1] - ind_noWk[j, 0] <= target_sampling_freq_Hz * 3:
                walking_labels[ind_noWk[j, 0] : ind_noWk[j, 1] + 1] = 1

    # Merge walking bouts if break less than 3 seconds
    ind_Wk = []
    walkLabel_1_indices = np.where(walking_labels == 1)[0]
    GSD_Output = []

    if walkLabel_1_indices.size > 0:
        ind_Wk = preprocessing.find_consecutive_groups(walking_labels == 1)
        # Create an empty list to store 'walk' dictionaries
        walk = []
        if ind_Wk.size > 0:
            for j in range(len(ind_Wk)):
                walk.append({"start": (ind_Wk[j, 0]), "end": ind_Wk[j, 1]})

        n = len(walk)

        for j in range(n):
            GSD_Output.append(
                {
                    "Start": walk[j]["start"] / target_sampling_freq_Hz,
                    "End": walk[j]["end"] / target_sampling_freq_Hz,
                    "fs": sampling_freq_Hz,
                }
            )
        print(f"{n} gait sequence(s) detected.")
    else:
        print("No gait sequence(s) detected.")

    # Create a DataFrame from the gait sequence data
    gait_sequences_ = pd.DataFrame(GSD_Output)
    gait_sequences_["onset"] = gait_sequences_["Start"]
    gait_sequences_["duration"] = gait_sequences_["End"] - gait_sequences_["Start"]
    gait_sequences_["event_type"] = "gait sequence"
    gait_sequences_["tracking_system"] = tracking_system

    # Check if the indices in ind_Wk are within the range of dt_data's index
    if ind_Wk.size > 0 and dt_data is not None:
        valid_indices = [index for index in ind_Wk[:, 0] if index < len(dt_data)]
        invalid_indices = len(ind_Wk[:, 0]) - len(valid_indices)

        if invalid_indices > 0:
            print(f"Warning: {invalid_indices} invalid index/indices found.")

        # Only use valid indices to access dt_data
        valid_dt_data = dt_data.iloc[valid_indices]

        # Create a DataFrame from the gait sequence data
        gait_sequences_ = pd.DataFrame(GSD_Output)
        gait_sequences_["onset"] = gait_sequences_["Start"]
        gait_sequences_["duration"] = (
            gait_sequences_["End"] - gait_sequences_["Start"]
        )
        gait_sequences_["event_type"] = "gait sequence"
        gait_sequences_["tracking_system"] = tracking_system

        # If original datetime is available, update the 'onset' column
        gait_sequences_["onset"] = valid_dt_data.reset_index(drop=True)

    # Create a DataFrame from the gait sequence data
    gait_sequences_ = gait_sequences_[
        ["onset", "duration", "event_type", "tracking_system"]
    ]

    # Return gait_sequences_ as an output
    self.gait_sequences_ = gait_sequences_

    # If Plot_results set to true
    # currently no plotting for datetime values
    if dt_data is not None and plot_results:
        print("No plotting for datetime values.")
        plot_results = False
        return self

    # Plot results if set to true
    if plot_results:
        # Convert detected_activity_signal from g back to m/s^2 for consistency
        detected_activity_signal *= 9.81

        viz_utils.plot_gait(
            target_sampling_freq_Hz, detected_activity_signal, gait_sequences_
        )

    return self