iir function takes a multi-channel signal and applies an IIR filter to
iir(df, sr, cutoff_freq, order = 4, type = "high", filter_type = "butter")
dataframe. The input multi-channel signal. The first column is timestamps in POSXlct format. The rest columns are signal values.
number. Sampling rate in Hz of the input signal.
number or numerical vector. The cutoff frequencies in Hz.
If the IIR filter is a bandpass or bandstop filter, it will be a 2-element
numerical vector specifying the low and high end cutoff frequencies
number. The order of the filter. Default is 4.
string. Filtering type, one of "low" for a low-pass filter, "high" for a high-pass filter, "stop" for a stop-band (band-reject) filter, or "pass" for a pass-band filter.
string. IIR filter type, one of "butter" for butterworth filter, "chebyI" for Chebyshev Type I filter, or "ellip" for Elliptic filter.
dataframe. Filtered signal.
This function filters the input multi-channel signal by applying an IIR
wiki for the
explanation of the filter. The implementations of IIR filters can be found in
For Chebyshev Type I, Type II and Elliptic filter, the passband ripple is fixed to be 0.05 dB. For Elliptic filter, the stopband ripple is fixed to be -50dB.
This function has been used as the main filtering method in MIMS-unit algorithm. Specifically, it uses a 0.5 - 5 Hz bandpass butterworth filter during filtering.
# Use sample data df = sample_raw_accel_data # View input illustrate_signal(df, plot_maxed_out_line = FALSE)# Apply filtering that uses the same setting as in MIMSunit algorithm output = iir(df, sr=80, cutoff_freq=c(0.2, 5), type='pass') # View output illustrate_signal(output, plot_maxed_out_line = FALSE)