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Added std_thresh_mg_start and std_thresh_mg_end parameters for DETACH #3

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Added std_thresh_mg_start and std_thresh_mg_end parameters for DETACH #3

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35 changes: 18 additions & 17 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -16,28 +16,29 @@ The DETACH algorithm is stored as a function called vertdetach within the /src/v
The function itself is defined as follows
```python
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)
temperature_freq=0.25, std_thresh_mg_start=8.0, std_thresh_mg_end=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. |
| 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_start | float | Optional (8.0) | The value which the standard deviation (STD) of an axis in the window must be below to trigger the start of non-wear |
| std_thresh_mg_end | float | Optional (8.0) | The value which the standard deviation (STD) of an axis in the window must be below to trigger the end of 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_roc_start | float | Optional (-0.2) | Temperature decrease rate-of-change threshold for non-wear start classification (see paper for more details) |
| temp_roc_end | float | Optional (0.1) | Temperature increase rate-of-change threshold for non-wear end 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.
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28 changes: 16 additions & 12 deletions src/vertdetach/vertdetach.py
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Expand Up @@ -4,8 +4,8 @@


def 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):
temperature_freq=0.25, std_thresh_mg_start=8.0, std_thresh_mg_end = 8.0, low_temperature_cutoff=26,
high_temperature_cutoff=30, temp_roc_start=-0.2, temp_roc_end=0.1, num_axes=2, quiet=False):
"""
Non-wear algorithm with a 5 minute minimum non-wear duration using absolute temperature, temperature rate of
change and accelerometer standard deviation of 3 axes to detect start and stop times of non-wear periods.
Expand All @@ -17,9 +17,12 @@ def vertdetach(x_values, y_values, z_values, temperature_values, accel_freq=75,
temperature_values (numpy array): temperature values
accel_freq (float): frequency of accelerometer in hz
temperature_freq (float): frequency of temperature sensor in hz
std_thresh_mg (float): the value which the std of an axis in the window must be below to trigger non-wear
std_thresh_mg_start (float): the value the std of an axis in the window must be below to trigger non-wear start
std_thresh_mg_end (float): the value the std of an axis in the window must be below to trigger non-wear end
low_temperature_cutoff (float): Low temperature for non-wear classification (see paper for details)
high_temperature_cutoff (float): High temperature cut off for wear classification (see paper for details)
temp_roc_start (float): The temperature rate of change used to determine non-wear start (see paper for details)
temp_roc_end (float): The temperature rate of change used to determine non-wear end (see paper for details)
num_axes (int): the number of axes that must be below the std threshold to be considered NW
quiet (bool): Whether or not to quiet print statements

Expand Down Expand Up @@ -61,7 +64,8 @@ def filter_signal(data, filt_type, low_f=None, high_f=None, sample_f=None, filte
vert_nonwear_end_datapoints = []

# mg to g
std_thresh_g = std_thresh_mg / 1000
std_thresh_g_start = std_thresh_mg_start / 1000
std_thresh_g_end = std_thresh_mg_end / 1000

# Create seies of 1 minute rolling std
x_std_fwd = pd.Series(x_values)[::-1].rolling(round(accel_freq * 60)).std()[::-1] # 1 minute forward looking STD
Expand All @@ -81,13 +85,13 @@ def filter_signal(data, filt_type, low_f=None, high_f=None, sample_f=None, filte
# Create a constant which converts accel datapoint to temp_datapoint

# Create DF column counting number of axes below std_thresh
std_df["Num Axes Fwd"] = np.sum(np.array([std_df["x_std_fwd"] < std_thresh_g,
std_df["y_std_fwd"] < std_thresh_g,
std_df["z_std_fwd"] < std_thresh_g], int), axis=0)
std_df["Num Axes Fwd"] = np.sum(np.array([std_df["x_std_fwd"] < std_thresh_g_start,
std_df["y_std_fwd"] < std_thresh_g_start,
std_df["z_std_fwd"] < std_thresh_g_start], int), axis=0)

std_df["Num Axes Backwards"] = np.sum(np.array([std_df["x_std_back"] < std_thresh_g,
std_df["y_std_back"] < std_thresh_g,
std_df["z_std_back"] < std_thresh_g], int), axis=0)
std_df["Num Axes Backwards"] = np.sum(np.array([std_df["x_std_back"] < std_thresh_g_end,
std_df["y_std_back"] < std_thresh_g_end,
std_df["z_std_back"] < std_thresh_g_end], int), axis=0)

# Find spots where at least num_axes are below the STD threshold for 90% of the next 5 minutes
std_df["Perc Num Axes >= num_axes for Next 5 Mins (fwd looking)"] = \
Expand Down Expand Up @@ -120,7 +124,7 @@ def filter_signal(data, filt_type, low_f=None, high_f=None, sample_f=None, filte

end_crit_1 = np.array(np.where((full_df["Num Axes Backwards"] == 0) &
(full_df["Perc Num Axes >= num_axes for Next 5 Mins (backward looking)"] <= 0.50) &
(full_df["Mean Five minute Temp Change"] > temp_inc_roc)))[0]
(full_df["Mean Five minute Temp Change"] > temp_roc_end)))[0]

# Second End Criteria: Absolute Temperature Path
end_crit_2 = np.array(np.where((full_df["Num Axes Backwards"] == 0) &
Expand All @@ -141,7 +145,7 @@ def filter_signal(data, filt_type, low_f=None, high_f=None, sample_f=None, filte

# Start Criteria 1: Rate of Change Path
if (full_df["Max Temp in next five mins"][start_ind] < high_temperature_cutoff) & (
full_df["Mean Five minute Temp Change"][start_ind] < temp_dec_roc):
full_df["Mean Five minute Temp Change"][start_ind] < temp_roc_start):
valid_start = True

# Start Criteria 2: Absolute Temperature Path
Expand Down