def bradykinesia(self, data_frame, method='fft'):
        """
            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

        """
        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])
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