## 6 Discussion
Moreover, when applying the wavelet transform, the authors have used different types of mother wavelets to produce the features. However, as depicted in Table 8, there is no consensus about the type of wavelet transform (i.e., CWT or DWT) or the kind of mother wavelet that can achieve an adequate signal representation and consequently improve the performance of the classification task. In recent studies, the Daubechies family of wavelets has been explored more frequently for feature extraction or signal representation, as shown in Table 8. Despite the gaps that wavelets currently have, this type of technique has also shown applications for denoisingpurposes, as presented by the study of [PERSON] et al. [8] and [PERSON] et al. [37], which suggest the broader range of applications that wavelet transform has in developing signal classification tasks. Nevertheless, one aspect that may mitigate the use of time-frequency methods is the computational cost they require compared to time and frequency domain based-features [26]. Aside from these feature extraction methods, other types of feature representations have been explored to a lesser extent, such as scale-invariant features, as presented in the study of [PERSON] et al. [32] where shapelets were used to generate scale-invariant features from the accelerometer \(z\)-axis. According to [PERSON]'s work, this type of method could potentially serve to compute not only local features but also global features from inertial sensor signals where typical time or frequency domain features are not suitable. However, another lacking aspect is that most studies do not report feature importance or feature selection methodology that could determine which of the computed features are associated with a given class through either a statistical test or importance score [80]. Additionally, factors that could affect the ability to detect or recognize road anomalies while collecting accelerometer or gyroscope data are human and hardware factors [81]. An example of a hardware factor are the sensitivities of the sensors embedded in the smartphone that could produce errors in the data collection and, consequently, in the training of learning-based techniques or the setting of thresholds [82]. Otherwise, an example of a human factor is the driver's behavior while driving that may differ across the set of drivers, which can introduce a source of variability [83]. The above aspects have not been considered in the literature that has developed road surface anomaly detection and classification systems. Thus, the performance of proposed algorithms could be prone to errors, and the relatively high performance that studies have reported could be mitigated. The above suggests future research directions that can be explored to reduce the effects of the scenarios mentioned earlier. Despite the diverse type of techniques that have been proposed, the problem of road surface anomaly detection and classification has been chiefly tackled to distinguish between a road in optimal condition versus lousy condition (e.g., pothole detection) or distinguish between different road anomalies (e.g., detection of potholes, speed bump, metal bumps, manholes) with one single detection or classification system. Nevertheless, the characterization of these road anomalies has not been extensively explored, as suggested by the study of [PERSON] et al. [17]. For example, vibration-based techniques could further explore and study the estimation of the pothole's depth or the speed bumps' state. The above can contribute to not only detecting the presence of the road anomaly but also providing information related to the characteristics of the anomaly and the degree of harm to the road surface with a low-cost system compared to 3D-reconstruction devices. Thus, there is still a gap that can be filled by exploring the use of algorithms that detect the road anomaly and characterize the quality of the anomaly or the structures present along the road surface.