CNN Approaches for Time Series Classification DOI: http://dx.doi.org/10.5772/intechopen.81170

recognition, as opposed to other subjects, Subject 6 within Study 2 had only one session recorded; thus, no CNN was trained for this subject. We observe that all frequency-domain CNNs (of all subjects in all studies) perform better than timedomain CNNs by 8.52% (in terms of the mean F1-score). This suggests that ST eliminates all noisy information and thus helps the CNN capture meaningful features.

However, as opposed to these results, comparing results of time and frequency domain CNNs on the Human Activity Recognition (HAR) task demonstrates the efficiency of time over frequency by 3.92% in terms of accuracy (as shown in Table 1). These contradictory results can be explained by the difference in the chosen ST frequency range for SMM recognition and that of HAR. Indeed, in SMM recognition, the frequency range of the ST was carefully chosen to cover almost all SMMs (0–3 Hz), resulting in optimal frequency-domain samples (containing full and noise-free information) which produced better CNN parameters. Meanwhile, the ST frequency range for HAR (0–8 Hz) may be a short/small range which generated frequency-domain samples that may have lost relevant information. Indeed human activity frequencies fall between 0 and 20 Hz (with 98% of the FFT amplitude contained below 10 Hz). Thus, in order to train CNNs with frequencydomain signals, it is necessary to analyze raw time series to come up with the proper ST frequency range which covers all valuable information needed for the recognition task.
