## 1 Introduction
Despite the progress made with these approaches, there is still a need for a more comprehensive and accurate system that can overcome the challenges faced in finger motion capture and note frequency recognition. Researchers have attempted to tackle these challenges through the use of various techniques, such as deep learning, neural networks, and signal processing. Section 2 highlights the different approaches and challenges described by researchers in this area, ranging from the use of wearable devices and image processing to the analysis of sound frequencies and the incorporation of machine learning. In order to capture finger motion, researchers have used wrist-wearable devices to capture the spatial coordinates of the guitarist's hand. Furthermore, the researchers discovered that the left-hand motion was never performed correctly and that pressure on the fretboard was required to generate the correct pitch frequency. Furthermore, the guitarist's students may struggle with either the proper pitch frequency or the correct left-hand movements. As a result, the work in this paper focuses on both in order to improve the performance of guitarists. Although Section 2 lists some works that have analyzed a guitarist's performance, some of them only focus on the finger motion capture portion, while others focus on analyzing sounds extracted from recorded songs. Managing figure motion and note frequency in real time is a challenge considered as a contribution to this research. To classify a guitarist's motion in space, researchers must deal with the guitarist's hand motion as either signals or as images. Researchers who have treated finger motion as a set of signals--more specifically, electromyographic signals--have obtained a time-series dataset ready for classification [12], and then used a random forest approach to classify the hand motion. The resulting accuracy indicated an improvement over previously described approaches [13]. An SVM approach was also tested for finger motion classification; however,it required more tuning than the random forest. The use of EMG signals can provide high accuracy, but limits the guitarist's movements. On the other hand, researchers have captured the guitarist's movement as images and then used a CNN for classification [14], which did not limit the guitarist's performance. However, the solution built using the CNN was not scalable, as it did not consider many exercises. In this paper, we propose a novel hybrid solution, which uses both finger motion capture (through MediaPipe) to extract finger landmark positions as time-series data, as well as note frequency recognition, in order to assess and improve a guitarist's overall performance. The reason that a hybrid approach was selected is that, when beginner guitarists start learning, they struggle with both playing the correct notes and with correct finger placement and technique. Guitar instructors can really help students with such problems, as they can both see and listen to the student's playing, allowing for the correction of both notes and technique. The hybrid approach proposed in this paper aims to mimic the action of an instructor through an educational program that corrects both the note played and the finger positioning using a microphone and a camera. The remainder of this paper is structured as follows: Section 2 reviews the most relevant research in the area of hand movement recognition and note frequency analysis. The proposed novel hybrid approach is described in Section 3. The experimental results and performance analysis are detailed in Section 4. Finally, in Section 5, our conclusions and directions for future work are discussed.