Specific Gestures Research Articles

Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network

Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.

Enhancing accessibility and assistance through eye gaze tracking: The I alert approach

The growing demand for assistive technologies for individuals with physical disabilities has led to significant advancements in human-computer interaction (HCI). Eye gaze tracking, a promising input modality, offers a non-invasive and intuitive way to enhance accessibility and interaction. This paper presents iAlert, an innovative eye gaze-based alert system designed to provide timely assistance to individuals with limited mobility or communication abilities. By analyzing eye movements, iAlert aims to detect user intent and trigger appropriate responses, thereby facilitating improved interaction with the environment, enhancing safety, and offering real-time assistance in everyday tasks. This system holds great potential for improving the quality of life for individuals with physical impairments, elderly individuals, and others requiring assistive technologies. The proposed methodology integrates eye gaze tracking with machine learning algorithms to build an intelligent alert system. Eye gaze data is captured using specialized hardware such as infrared sensors or cameras, which track the position and movement of the eyes. These gaze patterns are then pre-processed to remove noise and identify key features, such as fixations, saccades, and gaze direction. Machine learning models, specifically Convolutional Neural Networks (CNNs), are employed to classify the gaze data and predict user intent, allowing for real-time decision-making. A Support Vector Machine (SVM) classifier is used for detecting specific gestures or commands, such as a blink or a prolonged gaze, which are mapped to particular actions (e.g., triggering an alert, controlling a device, or communicating a need). The system adapts to user-specific behaviors over time through continuous learning, ensuring personalized assistance. The expected results from the iAlert system are twofold: first, a high level of accuracy in detecting and responding to user gaze commands, and second, an enhanced user experience in terms of real-time responsiveness and adaptability. The system is expected to achieve over 90% accuracy in identifying user intent, especially in controlled environments. The integration of machine learning algorithms like CNNs and SVM is critical for ensuring robust classification of eye gaze data, reducing the error rate in real-time applications. Moreover, the machine learning approach allows the system to continuously improve through adaptive learning, providing more accurate and personalized responses over time. The importance of using these algorithms lies in their ability to handle complex, non-linear relationships within gaze data, enabling the system to function effectively across different users and contexts. By leveraging deep learning techniques, the iAlert system can scale for a wide range of assistive applications, making it a valuable tool for enhancing independence and accessibility for individuals with physical challenges

AI-Powered Gesture-Controlled Interactive System for Real-Time Mathematical Problem Solving and Visualization

In this paper, we are working on an AI based gesture-controlled system which responses to the hand motion of user and solves computer generated mathematical problems without any need of mouse, keyboard or even getting off from their sit. Dubbed MathVision, the system uses contemporary machine learning techniques like OpenCV for detecting gestures and Google Gemini LLM technology for interpreting math equations—atop a relatively straightforward interface. A webcam or other camera captures hand movements on gestural drawings equations that are then converted into a usable format which can be interactively scrolled, deleted and confirmed by making specific gestures with the hands. A trained model to detect finger gestures when writing math expressions, with custom mathematical layers. MathVision serves as an educational software product, providing educational interactivity to a general process of problem-solving where students receive problems and can either solve mathematical exercises by hand or type their solutions. The purpose of this technology is to combine classical problem-solving approaches with the power new computational tools, so that math could be more interactive and effortless. MathVision converts visual manipulations into instant mathematical answers, and offers an interactive way to understand difficult math’s topics in a highly interactive manner. Combining gesture recognition with AI-based problem solving would change the future of our education; revolutionizing instruction and transmission of mathematical concepts across all levels.

The biomechanics of public speaking: Enhancing political communication and persuasion through posture and gesture analysis

Effective public speaking is critical in political communication, where non-verbal cues such as posture, gestures, facial expressions, and body movements significantly influence audience perception. Despite the known impact of these biomechanics, research in diverse cultural contexts remains limited. This study addresses this gap by examining the biomechanics of public speaking in the Philippines, focusing on 16 political speakers and 211 audience participants from varied demographic backgrounds. The study uses advanced motion capture technology, high-definition video recordings, and audience perception surveys to investigate how specific non-verbal elements affect audience engagement and perception. Challenges include accurately measuring and isolating the effects of these biomechanical factors amidst varied cultural and linguistic influences. The findings reveal that an upright posture significantly enhances perceived confidence (r = 0.72, p = 0.003) and trustworthiness (r = 0.65, p = 0.004), while high-frequency gestures positively correlate with clarity (β = 0.47, p = 0.008) and persuasiveness (β = 0.66, p = 0.003). Head movements, such as nodding, significantly increase audience engagement (F-value = 5.73, p = 0.002), and high-intensity smiling enhances emotional responses (t-value = 4.86, p = 0.001). These results underscore the importance of biomechanics in political communication, demonstrating that specific gestures and postures are critical in conveying confidence and persuasiveness. The study contributes to the field by offering insights into the strategic use of body language, particularly in multicultural settings, to enhance audience engagement and speaker effectiveness.

“SafeSteps” A Flutter -Based Application to Provide Security to Women’s

With women’s safety being one of the most serious and pressing issues worldwide, rapid technological advancements have created new opportunities to enhance personal security. This paper presents an improved mobile application that prioritizes women’s safety through a SIM card-based location tracking approach instead of relying on GPS services that require an internet connection. This feature ensures consistent and reliable location tracking even in areas with weak or no internet access. Inspired by existing platforms like bSafe and My Safetipin, the app includes an emergency alert system that allows users to trigger SOS signals to authorities and nearby registered users by pressing the power button, volume button, or using specific gestures. The involvement of nearby users aims to provide immediate assistance before law enforcement arrives, potentially preventing harm or saving lives. The paper features a survey and technical analysis detailing the app’s architecture, usability, and the benefits of SIM based tracking for improved response times during emergencies. Key Words: Women’s safety, SIM card location tracking, emergency alert system, SOS message, offline tracking, mobile safety app, user assistance network.

Hand Gesture Enabled Operation for Computer Using Deep Learning

This project aims to develop a system that detects hand gestures and prints corresponding instructions for computer operators. The system utilizes Deep learning algorithms CNN and mediapipe to recognize hand gestures, and then prints instructions for the operator to perform specific computer operations.Operations like Notepad Open,Shutdown,Volume Up,Volume Down etc.The system will consist of a webcam, a Deep learning model, and a printer. The webcam will capture images of hand gestures, which will be processed using the Deep learning model to identify the specific gesture. The system will then print the corresponding instruction on the printer. The system will be designed to be user-friendly and adaptable to different environments and hardware configurations.The system aims to improve efficiency, reduce errors, and enhance accessibility for computer operators. The proposed system consists of a hand gesture detection module, instruction printing module, and computer operation module. The system is trained using a dataset of hand gestures and corresponding computer operations. Experimental results show that the system achieves high accuracy in gesture recognition and instruction printing. The system has potential applications in various fields, including gaming, education, and healthcare. Key Words: Hand gesture detection, Deep learning, instruction printing, computer operation, accessibility, Mediapipe, CNN.

The Role of Kinesics in the Formation of Idiomatic Expressions in Russian and English

The article explores the influence of kinesic phenomena, such as gestures and body movements, on the development of idiomatic expressions in both languages. It examines how culturally specific gestures and physical actions are verbalized and incorporated into idiomatic phrases, reflecting the unique cultural perceptions and communicative practices of Russian and English speakers. By conducting a comparative linguistic analysis, the study identifies similarities and differences in the way kinesics is integrated into idiomatic expressions, highlighting the role of nonverbal communication in language formation and its impact on the semantic richness of idioms. The findings of this research underscore the importance of understanding the cultural and linguistic contexts in which idiomatic expressions are used, offering insights for translators, linguists, and intercultural communication specialists.

Wheelchair Basketball: morpho-functional and physiological aspects of the shoulder

Wheelchair basketball is one of the most popular paralympic sports. It is a great physical activity that guarantees important health benefits and promotes the well-being and social inclusion for disabled people. At the same time, wheelchair basketball involves a significant risk of injuries for wheelchair basketball players and upper limbs are the most burdened body segments. In particular, sport specific gestures lead to progressive overload of the shoulder joint, which usually results in shoulder pain. For these reasons, scientific and technological progress should improve the development of rehabilitative strategies to promote shoulder health and then to reduce shoulder pain. Within the spectrum of new technologies, electromyography is a tool that allows real-time measurement and to take advantage of the potentiality of biofeedback, so it has been used previously to improve the outcome of rehabilitation treatments. Therefore, the purpose of this review is to summarize and analyze the current evidence regarding the use of electromyography, as a rehabilitative strategy, to reduce shoulder pain in wheelchair basketball athletes. Following the PRISMA statement guidelines (PROSPERO - 2023, n° CRD42023470395), a total of 10 studies met the inclusion and exclusion criteria after a search of three databases (Pubmed, Google Scholar and Cochrane Library). The result of this review highlights the importance of electromyography and surface electromyography in the prevention and treatment of shoulder pain in wheelchair basketball athletes. Moreover, this examination emphasizes that using surface electromyography as a biofeedback seems to be an effective way to speed up shoulder pain rehabilitation outcomes in wheelchair basketball athletes. Keywords: exercise; sport; technologies; rehabilitation, physiology.

Sosialisasi Game Edukasi Virtual Quiz Berbasis Mediapipe untuk Sekolah Dasar

Virtual educational game quiz using Mediapipe is one of the innovative and exciting learning methods for 3rd grade elementary schoolchildren at SDN Wonorejo Talun Blitar. This game was created with the aim to help improve children's understanding and knowledge in a variety of subjects in an interactive and fun way. In this game, children will be asked to answer the questions given through video and voice displays using Mediapipe technology. They can answer questions by moving their bodies or showing answers through specific gestures. This will provide a more interactive and educational learning experience for children, so that they can more easily understand the lesson submitted. Besides, virtual educational quiz games can also help improve children's concentration and focus in learning. With a fun and exciting way of playing, children will be more motivated to learn and follow every question well. They will also learn to collaborate and communicate with their friends in search of the right answers. Through these games, children are expected to be able to learn more effectively and efficiently, as well as improve their academic scores at school. They will also become more skilled in using technology and understand lesson concepts better. Thus, a virtual educational quiz game using Mediapipe can be one of the effective and innovative learning methods for 3rd grade elementary schoolchildren at SDN Wonorejo Talun Blitar. For the outdoors proposed by the executive team are publications of scientific journals and virtual games quizzes.

Effect of Laterality in Microsurgery: Comparative Study of an Expert and a Novice.

Background: Hand laterality has an impact on surgical gestures. In this study, we sought to measure the multi-parameter variability of the microsurgical gesture depending on the hand used and the differences between expert microsurgeons and novices. Methods: Ten experienced microsurgeons and twenty medical students with no prior microsurgical experience performed arterial anastomosis on a chicken wing artery using dominant and non-dominant hands. We measured time and force using a homemade force-sensing microsurgical needle holder, heart rate variability with a Polar H10 chest strap, anxiety with the STAI-Y questionnaire and anastomosis quality using the MARS 10 scale. Results: In the microsurgeons' group, duration of anastomosis (p = 0.037), force applied to the needle holder (p = 0.047), anxiety (p = 0.05) and MARS10 (p = 0.291) were better with the dominant hand. For novices, there was no difference between the dominant and non-dominant hand pertaining to force, time and stress level. There were no differences between microsurgeons and novices pertaining to force and anxiety using the non-dominant hand. Conclusions: The study highlighted a marked laterality among microsurgical experts, a finding that may be explained by current learning methods. Surprisingly, no laterality is observed in students, suggesting that for a specific gesture completely different from everyday tasks, laterality is not predefined. Ambidexterity training in the residency curriculum seems relevant and may help microsurgeons improve performance and postoperative outcomes.

Real-Time Remote-Controlled Human Manipulation Medical Robot Using IoT Module

INTRODUCTION: Innovative robotics and advanced computer vision technology converge in the Human Manipulation-Controlled Robot, utilized for medical applications. The robot operates through human gestures, and includes a camera module for real-time visual feedback, enhancing its functionality and user interaction. OBJECTIVES: The primary goal of the research was to harness the natural expressiveness of human gestures to provide a more intuitive and engaging method of controlling medical robots. The focus is on enabling precise control through programmed responses to specific gestures, ensuring effective interaction with medical tasks. METHODS: The robot’s hardware configuration consists of a mobile platform with motorized components, an ESP32 module, gesture recognition sensors and a camera modules. The ESP32 module interprets signals from gesture recognition sensors to execute precise commands for the robot's movements and actions. Simultaneously, the camera module captures live footage, providing visual feedback through an intuitive interface for seamless interaction. RESULTS: The Human Manipulation-Controlled Robot has been successfully developed, featuring a fetch arm capable of autonomous movement and object manipulation. This research address critical needs in medical centers, demonstrating the feasibility of using only minimalistic EEG electrode wireless transmission to operate a robot effectively. CONCLUSION: Through the provision of a more intuitive and engaging method of controlling and interacting with medical robots, this innovation has the potential to significantly improve user experience. It represents a most important development in medical robotic vehicles, enhancing user experience and operational efficiency through advanced human-robot interaction techniques.

AI Virtual Mouse System Using Computer Vision to avoid COVID-19 spread

In the contemporary digital landscape, enhancing human-computer interaction efficiency and intuitiveness is essential. Traditional input devices like mice and keyboards are being augmented by innovative approaches such as hand gesture recognition, which provides a more natural method of interaction. This paper aims to generate a virtual mouse controlled by hand gestures using computer vision and deep learning techniques. The system employs a webcam to capture live video of the user's hand movements. These movements are analyzed using convolutional neural networks (CNNs) to identify specific gestures, which are then translated into mouse operations like cursor movement, clicking, and scrolling. This solution is hardware-independent, utilizing only the device's camera, making it accessible and straightforward to use. The goal is to create a seamless and efficient interaction method, allowing users to control their computers with simple hand gestures from a distance. Keywords: Convolutional Neural Network, Deep Learning, Hand Gesture Recognition, Virtual Mouse, Computer Vision, OpenCV

Virtual Mouse

Now a days the use of hand gesture recognition became popular due to the advance technology of artificial Intelligence (AI). In recent years, there has been a growing interest in developing alternative methods for human-computer interaction (HCI) that go beyond traditional input devices like keyboards and mouse. Hand gesture recognition has emerged as a promising approach, offering intuitive and natural ways for users to interact with digital interfaces. The proposed system utilizes computer vision techniques to detect and track hand gestures in real-time. By mapping specific gestures to mouse control commands, users can navigate and interact with graphical user interfaces (GUIs) without the need for physical input devices. In this paper A hand gesture- controlled virtual mouse system utilizes the AI and Machine learning algorithms to recognize the proper hand gestures and translate them into the mouse movements. The system we are designed that the people who have problems or difficulty using a traditional mouse or keyboard it will be appropriate for them. For this we are using camera that capture images of the user’s hand, which are processed by an AI and ML algorithm to recognize the gesture of hands being made. We trained the system by using a dataset of hand gestures. Keywords — Hand gesture recognition, Machine Learning, Artificial Intelligence, Virtual Mouse, Python, Media Pipe

HANDVIBE: HAND GESTURE VOLUME AND BRIGHTNESS CONTROL

Gesture Recognition has become increasingly relevant in the field of human-computer interaction, as it is a natural way to convey information. We aim to create a system that can identify specific human gestures and utilize them to transmit information for device control. This enables users to operate a computer by performing a specific gesture in front of the camera. The below approach can detect multiple hands simultaneously, that is left and right, each having its own purpose. It is detected by a standard webcam, and requires no extra equipment. The left hand is responsible in controlling the brightness and the right hand will be responsible for controlling the volume. Here we have used different computer vision techniques which includes border detection, and convex-hull detection. The system was able to detect the distance between two points present in the hand, namely fingertip of thumb as well as index finger. Then it calculates the distance between them which is used to apply for volume and brightness, i.e. If the fingers are pinched, the distance between them becomes “zero”, thereby setting the volume or brightness to zero. If it has the maximum distance ,then the volume or brightness is set to maximum that is “100”.The Volume or brightness is decided based upon the hand. The primary aim here is to enable users to adjust the volume as well as the brightness of their system with ease, either by increasing or decreasing it. This offers a promising alternative to touch-based controls as well as voice-based controls. Key Words: Hand Gesture, Volume Control , Brightness Control, Human Control Interaction, Edge Detection, OpenCV, Computer Vision

Manipulator Control System Based on Gesture Recognition for Space Operation

In the scenario of on-orbit servicing, the complex space environment and the strict limitation of spacesuits made the manual control of manipulator extremely inconvenient. Current remote-control methods such as voice control, are commonly affected by the signal ratio, especially in the case of solar wind. Therefore, in this paper, we proposed a gesture-based remote-control system for real-time manipulation of the space manipulator in order to accomplish the onboard tasks. Gesture-based control strategy is more friendly on human-machine interaction than voice control or manual control. The proposed method: utilized gesture recognition technique to perform scheduled tasks according to the preset gestures. For preset missions, specific gestures are recognized and matched through KNN algorithm, and then manipulator is driven to execute the scheduled tasks. To validate the efficiency of the proposed method, extensive experiments are carried out on simulation platforms and compared with the traditional method of gesture recognition using rotation angle and gesture recognition using CNN. Results demonstrated that our method outperforms the other two solutions in terms of accuracy and speed.

OBT-Trace: An approach to trace and recognition object motion through prosthetic hand gestures

The development of prosthetic hands has advanced significantly in recent years, aiming to provide more intuitive and functional solutions for individuals with upper limb amputations. This research presents a novel approach to prosthetic hand control by integrating 3D hand gesture recognition with object manipulation and recognition capabilities. Our proposed system utilizes a pre-trained object recognition model, based on transfer learning, to enable the prosthetic hand to perceive and identify objects in its vicinity. The model leverages a vast dataset of objects, enabling the prosthetic hand to recognize a wide array of everyday items, thus enhancing its versatility.In addition, the prosthetic hand incorporates a sophisticated 3D hand gesture recognition system, allowing users to control the hand's movements and actions seamlessly. By recognizing specific gestures, such as grasping, lifting, and releasing, users can intuitively interact with their environment and perform various tasks with ease. This research leverages the synergy between gesture recognition and object recognition, creating a powerful framework for prosthetic hand control. The system's adaptability and versatility make it suitable for a broad range of applications, from assisting with daily tasks to enhancing the quality of life for individuals with upper limb amputations.The results of this study demonstrate the feasibility and effectiveness of combining 3D hand gesture recognition with pre-trained object recognition through transfer learning. This approach opens up new possibilities for enhancing prosthetic hand functionality and usability, ultimately improving the lives of those who rely on these devices for daily living. The proposed model combines the features of YOLO V7 object detection with pre-trained models. The proposed model achieves 99.8% of accuracy compared to the existing models.

Introducing Virtual Reality in a STEMI Coronary Syndrome Course: Qualitative Evaluation with Nurses and Doctors.

In the increasing number of medical education topics taught with virtual reality (VR), the prehospital management of ST-segment elevation myocardial infarction (STEMI) had not been considered. This article proposes an implemented VR system for STEMI training and introduces it in an institutional course addressed to emergency nurses and case manager (CM) doctors. The system comprises three different applications to, respectively, allow (a) the course instructor to control the conditions of the virtual patient, (b) the CM to communicate with the nurse in the virtual field and receive from him/her the patient's parameters and electrocardiogram, and (c) the nurse to interact with the patient in the immersive VR scenario. We enrolled 17 course participants to collect their perceptions and opinions through a semistructured interview. The thematic analysis showed the system was appreciated (n = 17) and described as engaging (n = 4), challenging (n = 5), useful to improve self-confidence (n = 4), innovative (n = 5), and promising for training courses (n = 10). Realism was also appreciated (n = 13), although with some drawbacks (e.g., oversimplification; n = 5). Overall, participants described the course as an opportunity to share opinions (n = 8) and highlight issues (n = 4) and found it useful for novices (n = 5) and, as a refresh, for experienced personnel (n = 6). Some participants suggested improvements in the scenarios' type (n = 5) and variability (n = 5). Although most participants did not report usage difficulties with the VR system (n = 13), many described the need to get familiar with it (n = 13) and the specific gestures it requires (n = 10). Three suffered from cybersickness.

Early gesture use predicts children's language development in South Korea: New evidence supporting the cross-cultural importance of pointing.

Research in the U.S. and other Western countries shows that children's early gesture use, which starts prior to verbal communication, is an important predictor of children's later language development. Despite increasing efforts to study gesture use in diverse contexts, most of our knowledge on the role of gesture is largely based on populations of Western countries. In this study, we add to the growing body of international research by examining gesture use by 31 mothers and their 14-month-old infants (12 girls) in South Korea and investigate the gestures used during interaction, and whether early gesture use at 14months predicts Korean children's later language skills at 36months. The results showed that in addition to using gestures observed in other cultural contexts, Korean mother-child dyads used culturally specific gesture (i.e., bowing), showing an early sign of socialization that starts with preverbal children. In addition, Korean infants' index-finger pointing, but not showing and giving, predicted their later receptive and expressive vocabulary skills at 36months, providing additional support for the importance of pointing in early language development.

Brave New GES World: A Systematic Literature Review of Gestures and Referents in Gesture Elicitation Studies

How do we determine highly effective and intuitive gesture sets for interactive systems tailored to end users’ preferences? A substantial body of knowledge is available on this topic, among which gesture elicitation studies stand out distinctively. In these studies, end users are invited to propose gestures for specific referents, which are the functions to control for an interactive system. The vast majority of gesture elicitation studies conclude with a consensus gesture set identified following a process of consensus or agreement analysis. However, the information about specific gesture sets determined for specific applications is scattered across a wide landscape of disconnected scientific publications, which poses challenges to researchers and practitioners to effectively harness this body of knowledge. To address this challenge, we conducted a systematic literature review and examined a corpus of N = 267 studies encompassing a total of 187,265 gestures elicited from 6,659 participants for 4,106 referents. To understand similarities in users’ gesture preferences within this extensive dataset, we analyzed a sample of 2,304 gestures extracted from the studies identified in our literature review. Our approach consisted of (i) identifying the context of use represented by end users, devices, platforms, and gesture sensing technology; (ii) categorizing the referents; (iii) classifying the gestures elicited for those referents; and (iv) cataloging the gestures based on their representation and implementation modalities. Drawing from the findings of this review, we propose guidelines for conducting future end-user gesture elicitation studies.

ARTIFICIAL NEURAL NETWORKS IMPLEMENTATION IN MOBILE ROBOTIC PLATFORM CONTROL SYSTEM

In the era of rapid technological advancement, when robotics and intelligent systems are becoming an integral part of everyday life, the importance of developing control systems for mobile robotic platforms using artificial neural networks becomes extremely high and relevant. This field not only has significant practical needs but also holds considerable potential for innovative development. The evolution of modern robotics and computational intelligence has necessitated the creation of more efficient and adaptive mobile robotic systems. A system and tools for controlling mobile robotic platforms using artificial neural networks (ANNs) have been developed in this work. By simulating the workings of a neural system, ANNs enable robots not only to react to input data but also to learn to solve complex tasks and adapt to changes in their environment. One of the key challenges in mobile platform control is the development of effective and intuitive interfaces that provide convenient and reliable interaction between the user and the robotic system. In this context, the use of hand gestures by humans represents a progressive and promising direction as it allows for the creation of the most natural and efficient means of control. The main task is to create an effective and intuitively understandable system that enables the operator to interact with the robotic platform using natural movements and gestures. As a result, software with a graphical interface for real-time gesture recognition using machine learning has been developed. The scientific novelty of the approach is the integration of advanced ANNs methods to improve the quality of control and functionality of mobile robotic platforms. The main aspects of scientific novelty include integration with artificial intelligence, interactivity of control, development of robotics mobility, and adaptability to various tasks. The problem addressed in this work lies in the need to develop effective and intuitive control systems for mobile robotic platforms using gesture recognition technologies. A program based on convolutional neural networks has been developed, which determines the position of the hand and identifies specific gestures such as forward, backward, right and left turns, as well as stopping movement. The implemented technology can be used in various fields of human activity (smart home control, technological solutions for people with physical disabilities, enhancing interactivity in entertainment devices, improving interfaces for interacting with technical equipment).