# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 4 Discussion  
To the best of our knowledge, this work represents one of the first works for multiclass crouch gait recognition using synthetic data. The 8 DoF model based on feature extraction in the joint space was reported in a previous work [37]. Unlike other well-studied biomechanical models [40,47], our 8 DoF model provides a reduction in degrees of freedom and is useful for an early analysis in addition to reducing the computational burden and data acquisition instrumentation. On the other hand, the synthetic gait data provided in this study correspond to the real gait data acquisition scenarios, including offset and noisy defects. Although experimental validation was only performed with the right lower limb, this approach can be extended in parallel to the movements of the left limb and hip. Given the complexity of experimental settings of real crouched gait subjects, we believe that this approach could be beneficial for overcoming the scarcity and low availability of data for gait recognition systems. There are several works related to the generation of synthetic gait data using GAN [23,27]; although the data are very similar to real data, some limitations such as possible instability and large amount of training data required should be addressed. The work reported in [23] presents RMSE values for hip and knee angles less than 0.2 degrees to predict real signals using \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**Pelvic (\(Q_{1}\) and \(Q_{2}\))**} & \multicolumn{13}{c}{**Right Lower Limb (\(Q_{3R}\), \(Q_{4R}\) and \(Q_{5R}\))**} & \multicolumn{13}{c}{**Left Lower Limb (\(Q_{3L}\), \(Q_{4L}\) and \(Q_{5L}\))**} \\ \hline
**Metric** & **KNN** & **NB** & **DA** & **DT** & **ANN** & **KNN** & **NB** & **DA** & **DT** & **ANN** & **KNN** & **NB** & **DA** & **DT** & **ANN** \\ \hline Acc & 92.64 & 95.60 & 96.96 & 93.60 & 95.76 & 99.20 & 99.52 & 97.52 & 99.20 & 97.63 & 99.20 & 98.96 & 99.76 & 99.28 & 98.28 \\ R & 82.47 & 89.33 & 92.69 & 82.61 & 89.10 & 98.09 & 98.59 & 93.86 & 98.23 & 98.53 & 98.11 & 97.11 & 99.36 & 98.11 & 95.40 \\ SP & 95.38 & 97.19 & 98.11 & 96.10 & 97.34 & 99.56 & 99.70 & 98.46 & 99.48 & 97.78 & 99.47 & 99.34 & 99.85 & 99.54 & 98.91 \\ P & 82.36 & 90.23 & 92.70 & 82.72 & 89.33 & 98.27 & 98.79 & 94.09 & 98.19 & 93.99 & 98.22 & 97.45 & 99.38 & 98.36 & 95.64 \\ FM & 80.88 & 88.85 & 92.34 & 81.92 & 88.64 & 98.06 & 98.61 & 93.77 & 98.15 & 95.41 & 98.08 & 97.16 & 99.34 & 98.14 & 95.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Classification performance average of the 5 gaits for all algorithms and 3 body regions (pelvic, right, and left lower limbs). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{13}{c}{**DT**} & \multicolumn{1}{c}{**ANN**} \\ \hline
**Metric** & **N** & **C1** & **C2** & **C3** & **C4** & **N** & **C1** & **C2** & **C3** & **C4** \\ \hline Acc & 98.80 & 99.20 & 99.40 & 99.60 & 99.40 & 98.40 & 97.80 & 98.40 & 98.60 & 98.20 \\ R & 96.33 & 97.26 & 99.00 & 98.57 & 99.41 & 94.43 & 96.14 & 93.52 & 95.66 & 97.23 \\ SP & 99.23 & 99.74 & 99.50 & 99.72 & 99.52 & 99.22 & 98.05 & 99.47 & 99.26 & 98.54 \\ P & 97.28 & 99.17 & 98.04 & 99.33 & 98.00 & 97.40 & 91.31 & 98.40 & 96.92 & 94.14 \\ FM & 96.68 & 98.13 & 98.44 & 98.89 & 98.59 & 95.64 & 93.59 & 95.63 & 96.08 & 95.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classification performance of 5 gaits using left lower limb with DT and ANN. LSTM. Although we can indirectly control the RMSE with parameters \(SNR\) and \(DAE\), for more joint angles, for future research we could synthesize crouch gait signals using GANs. Another related work synthesizing gait data is addressed for hip displacement [21], achieving 12% error, using a CFP-neural model and stop criterion the standard deviation instead of the RMSE, which somehow guarantees that the narrow synthetical signal falls within the band of the dataset distribution.