## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Subject Independent I**} & \multicolumn{6}{c}{**Subject Independent II**} & \multicolumn{6}{c}{**Subject Dependent**} \\ \hline  & & & **RA** & **RD** & **SA** & **SD** & **DW** & **RA** & **RD** & **SA** & **SD** & **LW** & **RA** & **RD** & **SA** & **SD** & **LW** \\ \hline \multirow{6}{*}{**S**-**} & \multirow{6}{*}{**S**-**} & RA & **324** & 0 & 25 & 0 & 24 & **330** & 0 & 5 & 0 & 38 & **370** & 0 & 0 & 0 & 3 \\  & & RD & 28 & 277 & 0 & 25 & 77 & 6 & **332** & 0 & 19 & 50 & 2 & **378** & 1 & 7 & 19 \\  & & SA & 12 & 0 & **316** & 0 & 5 & 46 & 1 & **232** & 0 & 54 & 10 & 0 & **317** & 0 & 6 \\  & & SD & 0 & 2 & 0 & **281** & 1 & 0 & 73 & 0 & **206** & 5 & 0 & 5 & 0 & **277** & 2 \\  & & LWP & 150 & 14 & 8 & 1 & **92** & 24 & 54 & 7 & 1 & **179** & 21 & 10 & 6 & 1 & **227** \\  & & LWf & 139 & 11 & 10 & 38 & **620** & 89 & 91 & 12 & 40 & **586** & 28 & 40 & 8 & 33 & **709** \\ \cline{2-15}  & & RA & **304** & 0 & 6 & 0 & 63 & **341** & 0 & 1 & 0 & 31 & **373** & 0 & 0 & 0 & 0 \\  & & RD & 0 & **389** & 0 & 2 & 16 & 0 & **384** & 0 & 2 & 21 & 0 & **403** & 0 & 2 & 2 \\  & & SA & 6 & 0 & **298** & 0 & 29 & 23 & 0 & **254** & 0 & 56 & 0 & 0 & **330** & 0 & 3 \\  & & SD & 0 & 0 & 0 & **274** & 10 & 0 & 0 & 0 & **262** & 22 & 0 & 0 & 0 & **284** & 0 \\  & & LWP & 11 & 4 & 5 & 0 & **245** & 4 & 5 & 3 & 0 & **253** & 11 & 12 & 8 & 0 & **234** \\  & & LWf & 1 & 15 & 2 & 15 & **785** & 9 & 8 & 4 & 17 & **780** & 1 & 1 & 0 & 0 & **816** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._In RA, using subject-independent paradigms with LDA did not result in accurate recognition (F1 score \(<\) 0.8) (Table 1). RA was highly misclassified as SA and LW (Table 2). In order for RA to be detected relatively accurate (F1 score \(\geq\) 0.8), utilizing LSTM in subject-independent paradigms appeared to be necessary. Within subject-independent paradigms, signal fusion provided the highest performance (F1 score = 0.91) when LSTM was trained on PD patients data, although there was not a statistically significant difference between signal fusion and feet data (\(p\) > 0.05). Training LSTM on able-bodied data provided F1 scores of 0.85-0.87 for this task, and no significant differences across signal sources were observed (\(p\) > 0.05). RA was best classified using the subject-dependent paradigm and LSTM, where all signal sources provided very accurate outcomes (F1 score = 0.94-0.97). Similar results were obtained for RD, where using LDA with subject-independent paradigms did not provide accurate detection of the locomotion (F1 score \(<\) 0.8) (Table 1). RD was highly confused with SD and LW (Table 2). However, using LSTM and signal fusion in these paradigms improved F1 scores to from 0.78 and 0.7 in subject-independent I and II to 0.95 in subject-dependent (\(p\) < 0.05). In subject-dependent paradigms, LDA appeared to provide relatively accurate outcomes using all signal sources (F1 score = 0.81-0.9) for classification of RD, and LSTM led to a highly accurate recognition (F1 score = 0.96-1). F1 scores of below 0.8 were reported for SA using LDA with subject-independent paradigms except when signal fusion with able-bodied training data were employed (F1 score = 0.91) (Table 1). SA was mostly confused with RA and LW (Table 2). However, LSTM led to F1 scores of 0.85-0.92 for feet and signal fusion. In subject-dependent paradigm, both LDA and LSTM appeared to provide improved outcomes for all signal sources relative to subject-independent paradigms. However, the differences were not always statistically significant. For instance, signal fusion appeared to provide comparable outcomes in subject-independent I relative to subject-independent (i.e., 0.91 versus 0.95) for classification of SA (\(p\) > 0.05). In SD, only a few signal sources provided relatively accurate (F1 score \(\geq\) 0.8) classification of this task using LDA and subject-independent paradigms (Table 1). SD was highly confused with RD (Table 2).