## 6 Simulation Results
### Requirements Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Requirement** & **Inspired by** & **([PERSON] et al., 2018) ID Method** & **IID** &
\begin{tabular}{c} **([PERSON] et al., 2018) NDO Method** \\ \end{tabular} & **INDO** \\ \hline
1 & Non-dependence of additional sensors & [11,13] & ✓ & ✓ & ✓ \\
2 & Low phase lag in the estimation & [15] & ✗ & ✓ & ✗ & ✓ \\
3 & Low sensor noise sensitivity & [15] & ✗ & ✗ & ✓ & ✓ \\
4 & Low sensitivity to BSIPs uncertainties & [12] & ✗ & ✓ & ✗ & ✓ \\
5 & The average percentage error must be lower & [12] & ✓ & ✓ & ✓ & ✓ \\  & than 20 to 22\% & & & & & \\  & when using accurate model parameters & & & & & \\
6 & Small error band & [10] & ✗ & ✓ & ✗ & ✓ \\
7 & Approximately 0.5 s of settling time or lower & [10] & ✗ & ✓ & ✗ & ✓ \\  & Overshoot of approximately 25\% or lower within the estimation & [10] & ✓ & ✓ & ✓ & ✓ \\
8 & \(R^{2}\) greater or approximately 0.935 for the hip joint and 0.924 & [11] & ✗ & ✗ & ✗ & ✓ \\  & for the knee joint & [11] & ✗ & ✗ & ✗ & ✗ \\
10 & \(\times\)RMSE lower than 8.74\% for & [11] & ✗ & ✗ & ✗ & ✗ \\  & the hip joint and 10.26\% for the knee joint & & & & & \\
11 & A maximum of 5\% error when moving & [28] & ✗ & ✗ & ✗ & ✓ \\  & just one joint, i.e., the distal one & & & & & \\
12 & Finite-time convergence & [29] & ✗ & ✓ & ✗ & ✓ \\
13 & Should not require calibration each time & [13] & ✓ & ✓ & ✓ & ✓ \\  & that the user wears the LLRR, i.e., it requires & & & & \\  & a maximum of one calibration per user. & & & & \\
14 & It works in all ROM, and the limiting & & & & \\  & angles of the joints must be configurable & & & & \\
15 & It works with slow exercises, executing between 1-25 & & & & \\  & repetitions per set & & & & \\
16 & It works with the following ranges of forces: 0 kg & & & & \\  & to 15 kg for the hip, 0 kg to 15 kg & & & & \\  & for the knee, and 0 kg to 10 kg for the ankle. & & & & \\
17 & It works having a maximum percentage of error in the range from & & & & \\  & \(\times\) to 3\% & & & & \\
18 & It works within a squat exercise & Case Study & ✗ & ✓ & ✗ & ✓ \\ \hline Total & & & 5 (27.8\%) & \
\begin{tabular}{c} 13 \\ (72.2\%) \\ \end{tabular} & 6 (33.3\%) &
\begin{tabular}{c} 16 \\ (88.9\%) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the performance of the human–robot interaction torque estimation methods, during simulations with \(\text{SNR}=40\) dB and 20% BSIPs uncertainties, according to the requirements defined in Section 2. The checkmark and cross-mark symbols represent whether the requirement is fulfilled or not, respectively. of rest, that is, without exerting an intentional torque. The identification system would take the data from the first iterations to adjust those measurements given by the physiotherapist, which were an initial value. It would adapt them to a functional value without having to measure them. Therefore, it would provide the LLRR with more \"intelligence\" as it would tune itself. Thus, it allows that in a second stage, once identifying the robot-human system, a known method is applied for torque estimation, which traditionally has the problem of being very dependent on an accurate model and therefore sensitive to BSIPs uncertainties. Still, we proposed two methods that reduce this uncertainty. It is essential to mention that the torque estimation is not necessary for the execution of the controller because, in this type of rehabilitation, it is the patient who imposes the torque on the robot and not the other way around.