## 6 Discussion
two techniques require a high-quality label dataset to generate the models and effectively train the algorithms. The above introduces a challenge since it could be complicated to gather a sufficient amount of label data that represent the distributions of all the types of road anomalies in a road or city. This problem was also noticed by [PERSON] et al. [14], in Ref. [49], and highlighted in Section 4. In these same studies, it was pointed out that there is a lack of publicly available datasets, so there is an opportunity to produce and generate datasets that can help to validate road anomaly detection and classification algorithms with a greater homogeneity. The set of algorithms that could be affected more directly by the lack of label data are deep learning techniques since they often require a large sample size to avoid overfitting problems [47]. The above limits the use of deep learning as an automatic feature extraction technique of raw accelerometer and gyroscope data. Despite the disadvantages that the use of deep learning techniques can present, it is essential to remark that there are techniques that could be used to alleviate the lack of training data. One of these techniques is transfer learning [73]. The advantage of the transfer learning framework is that it is proposed to use for initialization pretrained architectures. Thus, CNNs, such as GoogleNet [74], AlexNet [75], ResNets [76], or DenseNets [77], could be used to fine-tune their weighs by setting a low learning rate based on the new given training dataset. On the basis of the results of this literature review, transfer learning has not been explored extensively. Therefore, there is an opportunity to explore the use of this technique for road surface anomaly detection and classification based on inertial sensors. A potential problem of applying transfer learning through pre-trained CNNs is that this method requires significant computational power. Furthermore, even though transfer learning could be a feasible option when there are a lack of available training data, no exact methodology could help determine the minimum sample size required to apply a transfer learning approach. The above also highlights gaps that could be investigated in future work. Another area that needs further research is how feature extraction is performed. As mentioned by [PERSON] et al. [48] extracting features from accelerometer data is not a trivial task. Therefore, the literature has proposed multiple types of feature extraction in either the time-domain, frequency-domain, or time-frequency domain, as presented in Section 5. In general, it can be appreciated that the time and frequency domains enable efficiently extracting features based on the signal's statistics, such as the mean, mode, maximum value, minimum value, and moments. Nevertheless, every author proposed or chose to extract different feature types, so there is no standard that can guarantee good performance based on the collected features. Additionally, these features could depend on the quality and characteristics of the collected sample. This drawback limits the reproducibility of the methods in the current literature, especially in the studies based on machine learning algorithms due to their data dependency [78]. Related to time-frequency methods for feature extraction, an area of opportunity can be explored in two main aspects the techniques to construct the time-frequency representation and the way these time-frequency representations are parameterized. For example, in the study of [PERSON] et al. [40] the different hyperparameters of the STFT (i.e., window type, window length, window overlapping) were tested in combination with a CNN for road surface anomaly recognition; this work, in particular, is one of the few that tried to fulfill this gap. Hence, further comparisons can be made to take advantage of employing the STFT, the wavelet transform, or the Hilbert-Huang transforms for road surface anomaly detection and classification [79].