## 3 Results  
Within each training paradigm, comparing the F1 scores of signal sources showed improved performance using LSTM relative to LDA. Similarly, subject-dependent outperformed subject-independent paradigms. However, the differences were not always statistically significant. Superior performance of LSTM to LDA was most notable in LWp and when trained on able-bodied data where LDA provided F1 scores ranging 0.19-0.39, while LSTM significantly increased the outcomes to 0.84-0.9 (\(p<0.05\)) (Tables 1 and 2). Comparing F1 scores across subject-independent paradigms I and II did not demonstrate any statistically significant differences (\(p>0.05\)). However, improved performance of subject-dependent relative to subject-independent paradigms was observed. The significant improvement (12-133%) of subject-dependent versus subject-independent training was most notable when LDA was applied. For instance, F1 scores for the classification of RA improved from 0.56, 0.67, 0.44, and 0.63 to 0.78, 0.84, 0.79, and 0.92 for the feet, trunk-pelvis, forearms, and signal fusion respectively using subject-dependent versus subject-independent I. (\(p<0.05\)). Similar outcomes were obtained comparing subject-dependent versus subject-independent II (\(p<0.05\)). However, in SD, while the feet signals in subject-dependent paradigms outperformed that in subject-independent (0.91 versus 0.81 and 0.85) the differences were not statistically significant (\(p>0.05\)). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \multicolumn{14}{c}{**Subject Independent 1**} & \multicolumn{14}{c}{**Subject Independent II**} & \multicolumn{14}{c}{**Subject Dependent**} \\ \hline \hline  & & & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** \\ \hline \multirow{6}{*}{**S**} & & RA & **284** & 0 & 73 & 0 & 16 & **301** & 0 & 26 & 0 & 46 & **365** & 0 & 0 & 0 & 0 & 8 \\  & RD & 10 & **299** & 0 & 68 & 40 & 15 & **333** & 0 & 40 & 19 & 3 & **391** & 2 & 4 & 7 \\  & SA & 24 & 0 & **308** & 0 & 1 & 45 & 0 & **269** & 1 & 18 & 14 & 1 & **316** & 0 & 2 \\  & SD & 0 & 9 & 0 & **275** & 0 & 0 & 11 & 0 & **272** & 1 & 0 & 6 & 0 & **275** & 3 \\  & LWP & _69_ & 92 & 12 & 4 & **88** & 29 & 100 & 4 & 3 & **129** & 57 & 73 & 11 & 1 & **123** \\  & LWP & _261_ & 40 & 49 & 60 & **448** & 210 & 15 & 33 & 45 & **425** & 135 & 93 & 12 & 44 & **534** \\ \hline \multirow{6}{*}{**S**} & RA & **301** & 0 & 13 & 0 & 59 & **316** & 0 & 0 & 0 & 57 & **368** & 0 & 5 & 0 & 0 \\  & RD & 0 & **373** & 0 & 6 & 28 & 0 & **340** & 1 & 3 & 63 & 0 & **404** & 0 & 2 & 1 \\  & SA & 5 & 0 & **300** & 0 & 28 & 7 & 0 & 266 & 0 & 60 & 2 & 0 & **330** & 0 & 1 \\  & SD & 0 & 0 & 0 & **273** & 11 & 0 & 1 & 1 & **256** & 26 & 0 & 0 & 0 & **254** & 0 \\  & LWP & 3 & 4 & 14 & 0 & 244 & 13 & 18 & 1 & 1 & **232** & 36 & 1 & 1 & 0 & **227** \\  & LWP & 11 & 4 & 0 & 10 & **793** & 1 & 6 & 4 & 4 & **803** & 0 & 0 & 0 & 0 & **518** \\ \hline \end{tabular}
\end{table} 
Table 2: Confusion matrices for classification of target locomotor tasks using signals from varying body segments and across different classification algorithms and training paradigms. The number of correctly classified windows for the target transitions are shown in bold. Non bold values are indicative of misclassification.