# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 4 Discussion
Adding more neurons to the CPG model could improve the accuracy of the algorithms, and this approach could be extended to other joint angles. Although the results obtained with respect to the synthetic gait data are promising, there are opportunities to improve upon the method outlined to obtain accurate synthetic crouch gait data. In general, in healthcare scenarios, a total of 92% of models trained with synthetic data have a lower accuracy than those trained on real data [53]. So, a further evaluation is needed to probe robustness and confidence in clinical settings. Furthermore, it is important to note that the creation of any synthetic dataset must be in agreement with the original dataset [54]. Thus, this aspect of using synthetic data should be heavily analyzed. More complex algorithms that encompass additional characteristics could be incorporated. The design of efficient gait recognition systems in real-world settings is a challenging task for researchers. Several methods related to gait recognition have been tested, for the classification of [PERSON]'s disease. To this end, in [27], the authors report an accuracy greater than 90% using deep neural networks (DNNs), while k-nearest neighbors (KNN), in [26], achieve an accuracy between 96 and 99%. In similar research, using an IMMU-based recognition system, the algorithms were as follows: KNN, ANN, SVM, DT, and RF were tested, showing an accuracy greater than 80% in the last three [32]. On the other hand, synthetic gaits of the knee or of the ankle for a DNN- based controller for prosthetic legs have also been proposed [22]. Specifically, for the recognition of the crouch gait, the two supervised and unsupervised approaches have been tested. Cluster analysis of the crouch gait pattern in children with spastic diplegic cerebral palsy using the sparse K-means method has been presented in [9] and the underlying severity of different gait patterns of the gait of the crouch [33] has been proposed. In a related work, though not recent, recognition of crouch gait based on AW-ELM has been reported [34]. Three subjects with mild, moderate, and severe crouch cases were classified with an accuracy of up to 91%. Finally, a work for biometric crouch gait identification is the most related. The gait data were classified into four classes: normal, crouch-2, crouch-3, and crouch-4, using K-mean, KNN, and ANN. They used 30 samples from each data category for training and 20 data points for testing. For the normal class, the accuracy was 74.68%, 100%, and 100% for each algorithm, while for the crouch, the general accuracy is below 60% for KNN and ANN and 80% for the k-mean. For these collision studies, unlike our work, the main limitation is the size of the dataset. For our recognition approach as a feasibility study, we use the most common algorithms KNN, NB, DA, DT, and ANN. The overall accuracy for the tree body region is up by 92%, which is promising. As future directions of the research, we plan to evaluate the latest algorithms such as DNNs. On the other hand, the primary design challenge of any gait recognition system is to select the most significant and optimum gait features [55]. For this study, we use the basic gait features used for gait recognition purposes; however, it is possible to test other frequency-temporal domain features. Finally, we believe that the results presented here provide evidence that our crouch gait recognition, as an early detection system, is capable of supporting diagnosis and decision making. Further research on the gait synthetic data generation and classification algorithms is required. Broader real-world areas such as surveillance, robotics, biomechanics, and sports medicine, among others, could also benefit.