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metrics

Functions

compute_counts(data, sample_rate=1000, epoch_len=10, min_thresh=4, max_thresh=128)

Compute counts from raw accelerometer data.

Reference: https://doi.org/10.1038/s41598-022-16003-x

Parameters:

  • data (NDArray) –

    2-D raw accelerometer data [ts x axis] in G.

  • sample_rate (float, default: 1000 ) –

    Sampling rate in Hz. Defaults to 1000 Hz.

  • epoch_len (int, default: 10 ) –

    Epoch length in seconds. Defaults to 10.

  • min_thresh (int, default: 4 ) –

    Minimum threshold. Defaults to 4.

  • max_thresh (int, default: 128 ) –

    Maximum threshold. Defaults to 128.

Returns:

  • NDArray –

    npt.NDArray: 2-D counts data [epoch x axis] in counts.

Example

import numpy as np data = np.ones((100, 3)) * 10 compute_counts(data, sample_rate=30, epoch_len=1).shape[0] 3

Source code in physiokit/imu/metrics.py 
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def compute_counts(
    data: npt.NDArray, sample_rate: float = 1000, epoch_len: int = 10, min_thresh: int = 4, max_thresh: int = 128
) -> npt.NDArray:
    """Compute counts from raw accelerometer data.

    Reference: https://doi.org/10.1038/s41598-022-16003-x

    Args:
        data (npt.NDArray): 2-D raw accelerometer data [ts x axis] in G.
        sample_rate (float, optional): Sampling rate in Hz. Defaults to 1000 Hz.
        epoch_len (int, optional): Epoch length in seconds. Defaults to 10.
        min_thresh (int, optional): Minimum threshold. Defaults to 4.
        max_thresh (int, optional): Maximum threshold. Defaults to 128.

    Returns:
        npt.NDArray: 2-D counts data [epoch x axis] in counts.

    Example:
        >>> import numpy as np
        >>> data = np.ones((100, 3)) * 10
        >>> compute_counts(data, sample_rate=30, epoch_len=1).shape[0]
        3
    """

    # 1. Resample to 30 Hz
    data = resample_signal(data, sample_rate=sample_rate, target_rate=30, axis=0)

    # 2. Bandpass filter
    data = _count_bpf_filter(data)

    # 3. Rectify, & threshold
    data = np.abs(data)
    data[data < min_thresh] = 0
    data[data > max_thresh] = max_thresh
    data = np.floor(data)

    # 4. Downsample to 10 Hz by taking moving average (n=3)
    data = np.nanmean(data.reshape((-1, 3, data.shape[1])), axis=1)

    # 5. Find counts by summing over epoch length
    counts = np.zeros((data.shape[0] // (10 * epoch_len),) + data.shape[1:], dtype=int)
    for i in range(0, counts.shape[0]):
        counts[i] = np.sum(data[i * 10 * epoch_len : (i + 1) * 10 * epoch_len], axis=0)
    # END FOR

    return counts

compute_enmo(x, y, z)

Compute ENMO from x, y, and z accelerometer data.

Reference: https://doi.org/10.1371/journal.pone.0142533

Parameters:

  • x (NDArray) –

    x-axis accelerometer data (G).

  • y (NDArray) –

    y-axis accelerometer data (G).

  • z (NDArray) –

    z-axis accelerometer data (G).

Returns:

  • NDArray –

    npt.NDArray: ENMO data (G).

Example

import numpy as np compute_enmo(np.array([0]), np.array([0]), np.array([1])) array([0.])

Source code in physiokit/imu/metrics.py 
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def compute_enmo(x: npt.NDArray, y: npt.NDArray, z: npt.NDArray) -> npt.NDArray:
    """Compute ENMO from x, y, and z accelerometer data.

    Reference: https://doi.org/10.1371/journal.pone.0142533

    Args:
        x (npt.NDArray): x-axis accelerometer data (G).
        y (npt.NDArray): y-axis accelerometer data (G).
        z (npt.NDArray): z-axis accelerometer data (G).

    Returns:
        npt.NDArray: ENMO data (G).

    Example:
        >>> import numpy as np
        >>> compute_enmo(np.array([0]), np.array([0]), np.array([1]))
        array([0.])
    """
    enmo = np.maximum(np.sqrt(x**2 + y**2 + z**2) - 1, 0)
    return enmo

compute_tilt_angles(x, y, z, in_radians=True)

Compute tilt angles from x, y, and z accelerometer data.

Reference: https://doi.org/10.1371/journal.pone.0142533

Parameters:

  • x (NDArray) –

    x-axis accelerometer data

  • y (NDArray) –

    y-axis accelerometer data

  • z (NDArray) –

    z-axis accelerometer data

  • in_radians (bool, default: True ) –

    If True, return angles in radians. Defaults to True.

Returns:

Example

import numpy as np compute_tilt_angles(np.array([0]), np.array([0]), np.array([1]), in_radians=False) (array([0.]), array([0.]), array([90.]))

Source code in physiokit/imu/metrics.py 
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def compute_tilt_angles(
    x: npt.NDArray, y: npt.NDArray, z: npt.NDArray, in_radians: bool = True
) -> tuple[npt.NDArray, npt.NDArray, npt.NDArray]:
    """Compute tilt angles from x, y, and z accelerometer data.

    Reference: https://doi.org/10.1371/journal.pone.0142533

    Args:
        x (npt.NDArray): x-axis accelerometer data
        y (npt.NDArray): y-axis accelerometer data
        z (npt.NDArray): z-axis accelerometer data
        in_radians (bool, optional): If True, return angles in radians. Defaults to True.

    Returns:
        tuple[npt.NDArray, npt.NDArray, npt.NDArray]: Tilt angles in radians or degrees.

    Example:
        >>> import numpy as np
        >>> compute_tilt_angles(np.array([0]), np.array([0]), np.array([1]), in_radians=False)
        (array([0.]), array([0.]), array([90.]))

    """
    x2 = x**2
    y2 = y**2
    z2 = z**2
    factor = 1 if in_radians else 180.0 / np.pi
    angle_x = np.arctan2(x, np.sqrt(y2 + z2)) * factor
    angle_y = np.arctan2(y, np.sqrt(x2 + z2)) * factor
    angle_z = np.arctan2(z, np.sqrt(x2 + y2)) * factor
    return angle_x, angle_y, angle_z