# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 2 Background
### Related Work  
Several studies reporting the implementation and evaluation of control systems for robotic arm exoskeletons are found in the literature. Below, we discuss some of these works, which are summarized in Table 1.  
[PERSON] et al. [32] presented a bio-inspired control with the use of impedance control based on stiffness. The work relates human stiffness to that of the exoskeleton. The authors used a neural network to update the coefficients at run time, and the system measured the trajectory according to the user's stiffness variation. The control estimated the trajectory for the shoulder and the elbow, and the error obtained was similar for both estimated joints.  
In [33], the authors developed a control system based on linear/non-linear cascade regression to activate an arm exoskeleton (shoulder, elbow, and wrist) applied in rehabilitation. The authors extracted the characteristics of the signals with a focus on acquiring amplitude and performing self-regression. The experiments carried out had two groups of participants, one composed of users who had suffered a cerebrovascular accident (CVA) and the other composed of users without a history of stroke. The system achieved a similar performance with the two groups of participants in estimating the movement of the arm's joints (shoulder, elbow, and wrist).  
The authors of [34] developed a control for arm exoskeleton in which the prototype received the commands from a PC via wireless communication. The authors extracted the electromyographic signals in the time, frequency, and time-frequency domains and used the data collected to train a backpropagation neural network (BPNN). The proposed solution uses an embedded microprocessor to capture the signals and command the actuators; the PC processes the measured signal and defines the movement to be performed by the robotic arm. The authors assessed the position estimate using the elbow joint. Despite occasional errors, with peaks of up to 30%, the system achieved a good average result.  
In [35], the authors developed a control for elbow exoskeleton using BPNN. They extracted information on the frequency and amplitude of the signal and measured the accuracy in different scenarios. They also considered three situations of visual focus of attention of the user. Their goal was to assess the impact of the control estimate the movements with the user paying attention or not. The system was tested on the elbow joint, and the experiments showed that the control estimate improved with the lack of the user's attention to the movements.  
As Table 1 shows, our work stands out from the others for emphasizing implementation using embedded computing and employing a control technique that has a lower computational cost for defining the control parameters, enabling implementation in low-cost processors for real-time execution. This approach facilitates the construction of a robotic arm that does not depend on a PC for operation.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Feature** & **[32]** & **[33]** & **[34]** & **[35]** & **This Work** \\ \hline Control technique & Shiftness-based impedance & Linear regression trained with EMG and limb position & BPNN trained with EMG and limb position & Impedance and with EMG and limb position & Impedance and admittance control \\ Body limb & Arm & Arm & Arm & Arm & Arm \\ Tested joints & Elbow and shoulder & Elbow, shoulder & Elbow & Elbow & Elbow and shoulder \\ Embedded implementation & No & No & Partial & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related work.