vertdetach 1.0.2

DETACH (Device Temperature and Acceleration Change) algorithm for detecting non-wear from raw accelerometer data.

vertdetach

DETACH (Device Temperature and Acceleration Change) algorithm detects non-wear periods for body-worn accelerometers by integrating a ‘rate-of-change’ criterion for temperature into a combined temperature-acceleration algorithm.

Publication

Vert, A., Weber, K. S., Thai, V., Turner, E., Beyer, K. B., Cornish, B. F., Godkin, F. E., Wong, C., McIlroy, W. E., & Van Ooteghem, K. (2022). Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature. BMC Medical Research Methodology, 22. https://doi.org/10.1186/s12874-022-01633-6

Using the vertdetach Algorithm

The DETACH algorithm is stored as a function called vertdetach within the /src/vertdetach/vertdetach.py Python file.

The function itself is defined as follows

vertdetach(x_values, y_values, z_values, temperature_values, accel_freq=75,
           temperature_freq=0.25, std_thresh_mg=8.0, low_temperature_cutoff=26, high_temperature_cutoff=30,
           temp_dec_roc=-0.2, temp_inc_roc=0.1, num_axes=2, quiet=False)

Input Arguments

The input arguments and their definitions are listed in the table below:

Argument	Data Type	Optional (default value) or Required	Description
x_values	NumPy Array	Required	x-axis accelerometer values
y_values	NumPy Array	Required	y-axis accelerometer values
z_values	NumPy Array	Required	x-axis accelerometer values
temperature_values	NumPy Array	Required	Temperature values
accel_freq	float	Optional (75)	Frequency of the accelerometer in Hz
temperature_freq	float	Optional (0.25)	Frequency of the temperature in Hz
std_thresh_mg	float	Optional (8.0)	The value which the standard deviation (STD) of an axis in the window must be below to trigger non-wear
low_temperature_cutoff	float	Optional (26.0)	Low temperature threshold for non-wear classification (see paper for more details)
high_temperature_cutoff	float	Optional (30.0)	High temperature threshold for non-wear classification (see paper for more details)
temp_dec_roc	float	Optional (-0.2)	Temperature decrease rate-of-change threshold for non-wear classification (see paper for more details)
temp_inc_roc	float	Optional (0.1)	Temperature increase rate-of-change threshold for non-wear classification (see paper for more details)
num_axes	int	Optional (2)	Number of axes that must be below the STD threshold to be considered non-wear
quiet	bool	Optional (False)	Whether or not to quiet print statements.

Returns

The algorithm returns a tuple with two objects:

1. start_stop_df: A pandas DataFrame with the start and end datapoints of the non-wear bouts.
2. vert_nonwear_array: A numpy array with the same length of the accelerometer data marked as either wear (0) or non-wear (1)

Example

Example python code to determine the percentage of non-wear time from

from src.vertdetach.vertdetach import vertdetach
import numpy as np

# Load Data
accelerometer_vals = np.load("path\to\raw\accelerometer.npy")
temperature_values = np.load("path\to\raw\temperature.npy")
x_values = accelerometer_vals[0]
y_values = accelerometer_vals[1]
z_values = accelerometer_vals[2]

# Define Frequencies
accel_freq = 75
temperature_freq = 0.25

# Calculate Non-wear
start_stop_df, nonwear_array = vertdetach(x_values = x_values, y_values = y_values, z_values = z_values, 
                                          temperature_values = temperature_values, accel_freq = accel_freq, 
                                          temperature_freq = temperature_freq)

# Analysis
total_wear_time = np.sum(nonwear_array)
pct_worn = total_wear_time/len(nonwear_array) * 100

print("The device was not worn %s percent of the time" % pct_worn)

Version/Changelog

v1.0.3

• loosen dependency requirements to minimum version instead of minor version

v1.0.2

• Detecting accelerometer non-wear periods using change in acceleration combined with rate-of-change in temperature.

Installation

To install the latest release of vertdetach directly from GitHub using pip, run the following line in terminal or console:

pip install git+https://github.com/nimbal/vertdetach

To install a specific release, insert @v#.#.# after the repository name replacing with the tag associated with that release. For example:

pip install git+https://github.com/nimbal/vertdetach@v1.0.0

Include vertdetach as Package Dependency

To include the latest release of vertdetach as a dependency in your Python package, include the following string within the list alongside your other dependencies:

install_requires=['vertdetach@git+https://github.com/nimbal/vertdetach@[version]']

To include a specific release, replace [version] with the tag associated with that release.

vertdetach Package Dependencies

• numpy
• pandas
• scipy