code: def bradykinesia(self, data_frame, method='fft'):
          
          try:
              data_frame_resampled = self.resample_signal(data_frame)
              data_frame_dc = self.dc_remove_signal(data_frame_resampled)
              data_frame_filtered = self.filter_signal(data_frame_dc, 'dc_mag_sum_acc')
  
              if method == 'fft':
                  data_frame_fft = self.fft_signal(data_frame_filtered)
                  return self.amplitude_by_fft(data_frame_fft)
              else:
                  return self.amplitude_by_welch(data_frame_filtered)
          except ValueError as verr:
              logging.error("TremorProcessor bradykinesia ValueError ->%s", verr.message)
          except:
              logging.error("Unexpected error on TemorProcessor bradykinesia: %s", sys.exc_info()[0])
docs: This method calculates the bradykinesia amplitude of the data frame. It accepts two different methods, \
              'fft' and 'welch'. First the signal gets re-sampled, dc removed and then high pass filtered.
  
              :param data_frame: the data frame
              :type data_frame: pandas.DataFrame
              :param method: fft or welch.
              :type method: str
              :return ampl: the amplitude of the Bradykinesia
              :rtype ampl: float
              :return freq: the frequency of the Bradykinesia
              :rtype freq: float