## 5 Discussion  
Looking at the results, it is clear that using traditional rehabilitation approaches as a foundation, digitized rehabilitation treatment has the most potential to enhance recovery results. Patients must engage with the digital device in a way that comes naturally to them in order for computer-assisted treatments to be effective, and they must also obtain support from the computer system depending on how well they do throughout the rehabilitation  
Figure 4: Confusion matrix of the classification results. Figure 3: Projections of the EMG signals (for motions M1–M7) to the three principle components P1, P2 and P3. session. Gesture recognition has been proposed as a potential remedy for applications of the human-machine interface to alleviate this problem, indicating that digital rehabilitation treatment offers the greatest promise as a complement to traditional rehabilitation procedures and could improve rehabilitation outcomes [29]. To be effective, computer-assisted treatments require patients to interact with the digital device in a way that seems natural to them, while also receiving support from the computer system depending on their performance throughout the rehabilitation session. Gesture recognition has been explored as a feasible approach for human-machine interaction applications to overcome this issue. Gesture recognition based on EMG is a method that allows dynamic evaluation of muscle work in real time. EMG is focused on electrical signals from muscles that are controlled by the cerebral system and are generated during muscle contraction, the signal reflects physiological properties of the muscles; the EMG signal is the motor electrical activity of the muscles [58]. Nowadays, in rehabilitation, it is preferable to use surface EMG signals are recorded with non-invasive electrodes detected at the surface and to obtain information about the activation time or intensity of surface muscles. It is the simplest way to measure muscle activation patterns and understand the behaviour of the human body under normal and pathological conditions [59]. Scientists also identified the following advantages of surface EMG during rehabilitation: that the electrodes are easier to apply, the patient feels minimal discomfort or pain, skin stretching or allergy when using them, the signals are better reproduced and they are very suitable for various movement recognition and analysis during exercising or training. However, the surface electrodes, which, in most cases, cover a larger area, take more information from the adjacent muscles, which can affect the accurate measurement results, which is very important in physical training assessment, recovery effectiveness or movement recognition and analysis in pathological conditions, especially after stroke, brain trauma, or spinal cord injuries [60]. Recently, EMG-based hand gesture recognition systems have faced challenges such as categorisation encounters, poor classification accuracy, low generalisation ability, limited training data and lack of robustness, and long time to calibrate the system for new users [61]. The development of an existing robotic exoskeleton for upper limb and hand rehabilitation is already in progress. Currently, it is able to perform passive exercises or training during rehabilitation after a stroke or trauma. One of the gaps in the project is the absence of a convenient algorithm to enable exercises and training with active-assisted rehabilitation.