## 6 Results and Discussion
### Performance Evaluation Using K-Fold Cross-Validation Training and Subsequent Compression
Since Fast Gated Recurrent Neural Network8 (FastGRNN8) had a very low F1 score(in red), we took FastGRNN32 as the best model size under opportunity dataset. Likewise, FastGRNN8 was considered best for model size since BasicRNN showed a poor F1 score(in red) on the PAMAP2 dataset. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RNN Type**} & \multicolumn{6}{c}{**WHARET-FNOW**} & \multicolumn{6}{c}{**WHARET-SNOW**} \\ \cline{2-11}  & **Mean Train Acc.** & **Mean Val Acc.** & **Test Acc.** & **F1 Score** & **Model Size** & **Mean Train Acc.** & **Mean Val Acc.** & **Test Acc.** & **F1 Score** \\ \hline Basic RNN & 0.77 & 0.76 & 0.83 & 0.78 & 3.30 & 0.86 & 0.85 & 0.85 & 0.81 \\ \hline FastGRNN & 0.86 & 0.85 & 0.93 & 0.92 & 3.37 & 0.97 & 0.97 & 0.98 & 0.98 \\ \hline FastRNN & 0.99 & 0.99 & 0.99 & 0.99 & 3.30 & 0.99 & 0.99 & 0.99 & 0.99 \\ \hline UGRNN & 0.85 & 0.82 & 0.90 & 0.87 & 5.42 & 0.96 & 0.96 & 0.96 \\ \hline GRU & 1.00 & 0.98 & 0.99 & 0.99 & 8.30 & 0.99 & 0.98 & 0.99 \\ \hline LSTM & 0.96 & 0.94 & 0.96 & 0.95 & 10.80 & 0.99 & 0.99 & 0.99 \\ \hline Best Performance & \multicolumn{6}{c}{FRNN and GRU} & \multicolumn{6}{c}{FRNN, GRU and LSTM} \\ \hline Smallest Model & \multicolumn{6}{c}{FRNN and basketballNN} & \multicolumn{6}{c}{FRNN, BasicRNN} \\ \hline Memory Savings via Fast Gates & \multicolumn{6}{c}{2.52\(\times\) with respect to GRU, 327\(\times\) with respect to LSTM} \\ \hline Performance Deviation via Fast Gates & \multicolumn{6}{c}{Nil} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Performance analysis of RNNs for Mobile HEALTH (MHEALTH) Fully Nonoverlapping Window (FNOW) and Semi Nonoverlapping Window (SNOW) based Human Activity Recognition (HAR) task. Mean training accuracy, mean validation accuracy, test accuracy, and F1 score are expressed as normalized %, and model size is expressed in kilobytes. Text in red is the highest in each category, except those under model size, where text in red represents the smallest model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RNN Type**} & \multicolumn{6}{c}{**USCHAD-FNOW**} & \multicolumn{6}{c}{**USCHAD-SNOW**} \\ \cline{2-11}  & **Mean Train Acc.** & **Mean Val Acc.** & **Test Acc.** & **F1 Score** & **Model Size** & **Mean Train Acc.** & **Mean Val Acc.** & **Test Acc.** & **F1 Score** \\ \hline Basic RNN & 0.51 & 0.51 & 0.53 & 0.44 & 2.23 & 0.51 & 0.51 & 0.49 & 0.29 \\ \hline FastGRNN & 0.70 & 0.70 & 0.73 & 0.64 & 2.30 & 0.71 & 0.71 & 0.73 & 0.73 \\ \hline FastRNN & 0.77 & 0.76 & 0.79 & 0.73 & 2.24 & 0.87 & 0.87 & 0.90 & 0.86 \\ \hline UGRNN & 0.85 & 0.85 & 0.87 & 0.83 & 3.67 & 0.85 & 0.86 & 0.90 & 0.87 \\ \hline GRU & 0.84 & 0.83 & 0.84 & 0.80 & 5.11 & 0.86 & 0.84 & 0.88 & 0.84 \\ \hline LSTM & 0.75 & 0.75 & 0.70 & 0.64 & 6.55 & 0.79 & 0.76 & 0.74 & 0.67 \\ \hline Best Performance & \multicolumn{6}{c}{UGRNN} & \multicolumn{6}{c}{FastRNN} \\ \hline Smallest Model & \multicolumn{6}{c}{FRNN, FGRNN, BasicRNN} & \multicolumn{6}{c}{FRNN, FGRNN, BasicRNN} \\ \hline Memory Savings via Fast Gates & \multicolumn{6}{c}{1.63\(\times\) with respect to UGRNN} \\ \hline Performance Deviation via Fast Gates & \multicolumn{6}{c}{up to 10\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Performance analysis of RNNs on University of Southern California Human Activity dataset or USCHAD Fully Nonoverlapping Window (FNOW)- and Semi Nonoverlapping Window (SNOW)-based Human Activity Recognition (HAR) task. Mean train accuracy, mean validation accuracy, test accuracy,y and F1 score are expressed in normalized %, and model size is expressed in KB. Text in red is the highest in each category, except those for model size, where text in red represents the smallest model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**RNN Type**} & \multicolumn{6}{c}{**WHARET-FNOW**} & \multicolumn{6}{c}{**WHARET-SNOW**} \\ \cline{2-11}  & **Mean Train Acc. ** & **Mean Val Acc. ** & **Test Acc. ** & **F1 Score** & **Model Size** & **Mean Train Acc. ** & **Mean Val Acc.