This Paraschiv-Ionescu initial contact detection algorithm identifies initial contact in accelerometer data
collected from a low back IMU sensor. The purpose of algorithm is to identify and characterize initial contacts
within walking bouts.
The algorithm takes accelerometer data as input, and the vertical acceleration component, and processes each
specified gait sequence independently. The signal is first detrended and then low-pass filtered. The resulting
signal is numerically integrated and differentiated using a Gaussian continuous wavelet transformation. The
initial contact (IC) events are identified as the positive maximal peaks between successive zero-crossings.
Finally, initial contacts information is provided as a DataFrame with columns onset, event_type, and
tracking_systems.
Methods:
| Name |
Description |
detect |
Detects initial contacts on the accelerometer signal.
|
Examples:
Find initial contacts based on the detected gait sequence
>>> icd = ParaschivIonescuInitialContactDetection()
>>> icd = icd.detect(accel_data=acceleration_data, sampling_freq_Hz=100)
>>> print(icd.initial_contacts_)
onset event_type duration tracking_systems
0 5 initial contact 0 SU
1 5.6 initial contact 0 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: methodological challenges...
Source code in kielmat/modules/icd/_paraschiv.py
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186 | class ParaschivIonescuInitialContactDetection:
"""
This Paraschiv-Ionescu initial contact detection algorithm identifies initial contact in accelerometer data
collected from a low back IMU sensor. The purpose of algorithm is to identify and characterize initial contacts
within walking bouts.
The algorithm takes accelerometer data as input, and the vertical acceleration component, and processes each
specified gait sequence independently. The signal is first detrended and then low-pass filtered. The resulting
signal is numerically integrated and differentiated using a Gaussian continuous wavelet transformation. The
initial contact (IC) events are identified as the positive maximal peaks between successive zero-crossings.
Finally, initial contacts information is provided as a DataFrame with columns `onset`, `event_type`, and
`tracking_systems`.
Methods:
detect(accel_data, gait_sequences, sampling_freq_Hz):
Detects initial contacts on the accelerometer signal.
Examples:
Find initial contacts based on the detected gait sequence
>>> icd = ParaschivIonescuInitialContactDetection()
>>> icd = icd.detect(accel_data=acceleration_data, sampling_freq_Hz=100)
>>> print(icd.initial_contacts_)
onset event_type duration tracking_systems
0 5 initial contact 0 SU
1 5.6 initial contact 0 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: methodological challenges...
"""
def __init__(
self,
):
"""
Initializes the ParaschivIonescuInitialContactDetection instance.
"""
self.initial_contacts_ = None
def detect(
self,
accel_data: pd.DataFrame,
sampling_freq_Hz: float,
v_acc_col_name: str,
gait_sequences: Optional[pd.DataFrame] = None,
dt_data: Optional[pd.Series] = None,
tracking_system: Optional[str] = None,
) -> pd.DataFrame:
"""
Detects initial contacts 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.
v_acc_col_name (str): The column name that corresponds to the vertical acceleration.
gait_sequences (pd.DataFrame, optional): A dataframe of detected gait sequences. If not provided, the entire acceleration time series will be used for detecting initial contacts.
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:
ParaschivIonescuInitialContactDetection: Returns an instance of the class.
The initial contacts information is stored in the 'initial_contacts_' attribute,
which is a pandas DataFrame in BIDS format with the following columns:
- onset: Initial contacts.
- event_type: Type of the event (default is 'Inital contact').
- tracking_system: Tracking systems used the events are derived from.
"""
# Check if data is empty
if accel_data.empty:
self.initial_contacts_ = pd.DataFrame()
return self # Return without performing further processing
# 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 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 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
# Extract vertical accelerometer data using the specified index
acc_vertical = accel_data[v_acc_col_name]
# Initialize an empty list to store the processed output
processed_output = []
# Initialize an empty list to store all onsets
all_onsets = []
# Process each gait sequence
if gait_sequences is None:
gait_sequences = pd.DataFrame(
{"onset": [0], "duration": [len(accel_data) / sampling_freq_Hz]}
)
for _, gait_seq in gait_sequences.iterrows():
# Calculate start and stop indices for the current gait sequence
start_index = int(sampling_freq_Hz * gait_seq["onset"])
stop_index = int(
sampling_freq_Hz * (gait_seq["onset"] + gait_seq["duration"])
)
accv_gait_seq = acc_vertical[start_index:stop_index].to_numpy()
try:
# Perform Signal Decomposition Algorithm for Initial Contacts (ICs)
initial_contacts_rel, _ = preprocessing.signal_decomposition_algorithm(
accv_gait_seq, sampling_freq_Hz
)
initial_contacts = gait_seq["onset"] + initial_contacts_rel
gait_seq["IC"] = initial_contacts.tolist()
# Append onsets to the all_onsets list
all_onsets.extend(initial_contacts)
except Exception as e:
print(
"Signal decomposition algorithm did not run successfully. Returning an empty vector of initial contacts"
)
print(f"Error: {e}")
initial_contacts = []
gait_seq["IC"] = []
# Append the information to the processed_output list
processed_output.append(gait_seq)
# Check if processed_output is not empty
if not processed_output:
print("No initial contacts detected.")
return pd.DataFrame()
# Create a DataFrame from the processed_output list
initial_contacts_ = pd.DataFrame(processed_output)
# Create a BIDS-compatible DataFrame with all onsets
self.initial_contacts_ = pd.DataFrame(
{
"onset": all_onsets,
"event_type": "initial contact",
"duration": 0,
"tracking_systems": tracking_system,
}
)
# If original datetime is available, update the 'onset' column
if dt_data is not None:
valid_indices = [
index
for index in self.initial_contacts_["onset"]
if index < len(dt_data)
]
invalid_indices = len(self.initial_contacts_["onset"]) - 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]
# Update the 'onset' column
self.initial_contacts_["onset"] = valid_dt_data.reset_index(drop=True)
return self
|
Initializes the ParaschivIonescuInitialContactDetection instance.
Source code in kielmat/modules/icd/_paraschiv.py
| def __init__(
self,
):
"""
Initializes the ParaschivIonescuInitialContactDetection instance.
"""
self.initial_contacts_ = None
|
Detects initial contacts 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
|
v_acc_col_name
|
str
|
The column name that corresponds to the vertical acceleration.
|
required
|
gait_sequences
|
DataFrame
|
A dataframe of detected gait sequences. If not provided, the entire acceleration time series will be used for detecting initial contacts.
|
None
|
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:
| Name | Type |
Description |
ParaschivIonescuInitialContactDetection |
DataFrame
|
Returns an instance of the class.
The initial contacts information is stored in the 'initial_contacts_' attribute,
which is a pandas DataFrame in BIDS format with the following columns:
- onset: Initial contacts.
- event_type: Type of the event (default is 'Inital contact').
- tracking_system: Tracking systems used the events are derived from.
|
Source code in kielmat/modules/icd/_paraschiv.py
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186 | def detect(
self,
accel_data: pd.DataFrame,
sampling_freq_Hz: float,
v_acc_col_name: str,
gait_sequences: Optional[pd.DataFrame] = None,
dt_data: Optional[pd.Series] = None,
tracking_system: Optional[str] = None,
) -> pd.DataFrame:
"""
Detects initial contacts 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.
v_acc_col_name (str): The column name that corresponds to the vertical acceleration.
gait_sequences (pd.DataFrame, optional): A dataframe of detected gait sequences. If not provided, the entire acceleration time series will be used for detecting initial contacts.
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:
ParaschivIonescuInitialContactDetection: Returns an instance of the class.
The initial contacts information is stored in the 'initial_contacts_' attribute,
which is a pandas DataFrame in BIDS format with the following columns:
- onset: Initial contacts.
- event_type: Type of the event (default is 'Inital contact').
- tracking_system: Tracking systems used the events are derived from.
"""
# Check if data is empty
if accel_data.empty:
self.initial_contacts_ = pd.DataFrame()
return self # Return without performing further processing
# 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 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 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
# Extract vertical accelerometer data using the specified index
acc_vertical = accel_data[v_acc_col_name]
# Initialize an empty list to store the processed output
processed_output = []
# Initialize an empty list to store all onsets
all_onsets = []
# Process each gait sequence
if gait_sequences is None:
gait_sequences = pd.DataFrame(
{"onset": [0], "duration": [len(accel_data) / sampling_freq_Hz]}
)
for _, gait_seq in gait_sequences.iterrows():
# Calculate start and stop indices for the current gait sequence
start_index = int(sampling_freq_Hz * gait_seq["onset"])
stop_index = int(
sampling_freq_Hz * (gait_seq["onset"] + gait_seq["duration"])
)
accv_gait_seq = acc_vertical[start_index:stop_index].to_numpy()
try:
# Perform Signal Decomposition Algorithm for Initial Contacts (ICs)
initial_contacts_rel, _ = preprocessing.signal_decomposition_algorithm(
accv_gait_seq, sampling_freq_Hz
)
initial_contacts = gait_seq["onset"] + initial_contacts_rel
gait_seq["IC"] = initial_contacts.tolist()
# Append onsets to the all_onsets list
all_onsets.extend(initial_contacts)
except Exception as e:
print(
"Signal decomposition algorithm did not run successfully. Returning an empty vector of initial contacts"
)
print(f"Error: {e}")
initial_contacts = []
gait_seq["IC"] = []
# Append the information to the processed_output list
processed_output.append(gait_seq)
# Check if processed_output is not empty
if not processed_output:
print("No initial contacts detected.")
return pd.DataFrame()
# Create a DataFrame from the processed_output list
initial_contacts_ = pd.DataFrame(processed_output)
# Create a BIDS-compatible DataFrame with all onsets
self.initial_contacts_ = pd.DataFrame(
{
"onset": all_onsets,
"event_type": "initial contact",
"duration": 0,
"tracking_systems": tracking_system,
}
)
# If original datetime is available, update the 'onset' column
if dt_data is not None:
valid_indices = [
index
for index in self.initial_contacts_["onset"]
if index < len(dt_data)
]
invalid_indices = len(self.initial_contacts_["onset"]) - 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]
# Update the 'onset' column
self.initial_contacts_["onset"] = valid_dt_data.reset_index(drop=True)
return self
|