# A Review on Upper Limb Rehabilitation Robots
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Robot**} & \multirow{2}{*}{**Ternary Type**} & **Post-Stroke** & **No. of** & **No. of** & **Improvements** \\  & & & **Time** & **Subjects** & **Sessions** & **Improvements** \\ \hline
[20] & MIT-MANUS & \multirow{2}{*}{Assistive} & \multirow{2}{*}{\(<\)6 months} & \multirow{2}{*}{96} & 25 sessions & motor power of the shoulder & \\  & & & & & 1 h/workday & \multicolumn{1}{c}{} & and the elbow was \\  & & & & & 1 h/workday & \multicolumn{1}{c}{} & significantly improved \\ \hline
[38] & MIME & \multirow{2}{*}{Assistive} & \multirow{2}{*}{\(>\)6 months} & \multirow{2}{*}{27} & 24 sessions & the reach ability and the & \\  & & & & & 8 weeks & proximal arm strength were \\  & & & & & 1 h/workday & \multicolumn{1}{c}{} & largely improved \\ \hline
[41] & ARM Guide & \multirow{2}{*}{Assistive} & \multirow{2}{*}{\(>\)6 months} & \multirow{2}{*}{3} & 24 sessions & motion velocity and range of \\  & & & & & 8 weeks & motion were greatly \\  & & & & & 1 h/workday & improved \\ \hline
[29] & ARMin & Passive & \(>\)6 months & \multirow{2}{*}{3} & 40 sessions & significant improvement in \\  & & & & & 8 weeks & muscle strength, arm motion \\  & & & & & 1 h/workday & \multicolumn{1}{c}{} & and other functional tasks \\ \hline
[40] & CADEN-7 & Passive-Assistive & - & - & - & \multicolumn{1}{c}{} & Unverified No clinical study \\  & & & & & & 18 sessions & was carried out on CADEN-7 \\ \hline
[48] & L-EXOS & Passive-Assistive & \(>\)6 months & \multirow{2}{*}{9} & 18 sessions & \multicolumn{1}{c}{} & Results showed a relative \\  & & & & & 6 weeks & improvement in three tasks: \\  & & & & & 1 h/workday & \multicolumn{1}{c}{} & reaching task, motion task \\  & & & & & & 1 h/workday & \multicolumn{1}{c}{} & and object manipulating \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Clinical results of the rehabilitation robots. The relationship between the EMG signal and the torque has been studied and explained by many researchers. Reference [73] used a recurrent artificial neural network (RANN) to investigate the relationship between the EMG signal and the required torque at the elbow joint under volunteer efforts. The EMG signal and the kinematic data--specifically, the angle and angular velocity--were used as inputs to expect the required torque. The role of the kinematic data was investigated to check its effect on the predicted torque [78]. The EMG signal with kinematic joint inputs was firstly used to predict the torque; then, only the EMG signal was used to predict the torque, and the comparison between the two approaches was investigated. The study involved recruiting six healthy subjects with three different loads (0 kg, 1 kg and 2 kg) held by the hand at two different positions: elbow flexion (90\({}^{\circ}\)) and full elbow extension (0\({}^{\circ}\)). The approach was performed at two different angular velocities (60\({}^{\circ}\)/s and 90\({}^{\circ}\)/s) [79]. The subjects were trained to perform the aforementioned procedure, and the root mean square error (RMSE) was calculated between the expected and the predicted torques for each procedure. The results showed that the RMSE when using the EMG signal and the kinematic data was 0.17 \(\pm\) 0.03 Nm for the training dataset and 0.35 \(\pm\) 0.06 Nm for the test dataset. Furthermore, the RMSE when using the EMG alone was 0.57 \(\pm\) 0.07 Nm for the training dataset and 0.73 \(\pm\) 0.11 Nm for the test dataset [73]. Therefore, using the EMG and the kinematic data as inputs for estimating the torque showed better performance [79]. To conclude, EMG-driven robots are considered the better choice to rehabilitate impaired limbs when compared to normal robots [8].