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Artykuły w czasopismach na temat „And MediaPipe”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

Ratusaria, Rahul, Tushar Baghel, Ayush Chander Vanshi, and Neeraj Garg. "GYM REP TRACKER USING MEDIAPIPE AND PYTHON." International journal of multidisciplinary advanced scientific research and innovation 1, no. 10 (2021): 240–45. http://dx.doi.org/10.53633/ijmasri.2021.1.10.001.

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Human Pose estimation has grabbed the eye of the computer vision community for the past few decades. It is a vital step closer to knowledge people in pics and motion pictures. Strong articulations, small and hardly visible joints, occlusions, apparel, and lighting changes make it very difficult to perform estimate pose. Human Pose estimation is an important problem that needed to be study. It is used to detect human anatomical key points (e.g., shoulder, elbows, legs, wrist, etc.) in real time using less computational resources. There are many Artificial Intelligence models i.e, Posenet, OpenP

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Nguyen, Phuoc Thanh, Thanh Hoang Nguyen, Ngoc Xuan Nguyen Hoang, Huynh Thanh Binh Phan, Hoang Son Hai Vu, and Hieu Nhan Huynh. "Exploring MediaPipe optimization strategies for real-time sign language recognition." CTU Journal of Innovation and Sustainable Development 15, ISDS (2023): 142–52. http://dx.doi.org/10.22144/ctujoisd.2023.045.

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The present study meticulously investigates optimization strategies for real-time sign language recognition (SLR) employing the MediaPipe framework. We introduce an innovative multi-modal methodology, amalgamating four distinct Long Short-Term Memory (LSTM) models dedicated to processing skeletal coordinates ascertained from the MediaPipe framework. Rigorous evaluations were executed on esteemed sign language datasets. Empirical findings underscore that the multi-modal approach significantly elevates the accuracy of the SLR model while preserving its real-time capabilities. In comparative anal

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G, Dr Thanigavel, Keerthika D, Harini P, Dilli Raj M, and Sonali D. "Smart Fit: Innovative Solutions to Enhance Fitness Activities and Promote a Healthy Lifestyle." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3229–37. https://doi.org/10.22214/ijraset.2025.68961.

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Abstract: Fitness tracking and guidance have evolved significantly with advancements in artificial intelligence and computer vision. This project, titled "SMART FIT: Innovative Solutions To EnhanceFitness Activities And Promote a Healthy Lifestyle" harnesses the power of OpenCV and Mediapipe to provide real-time feedback on workout accuracy. By leveraging Mediapipe's pose estimation model, the system effectively tracks skeletal landmarks and analyzes body posture during exercises. This technology enables a seamless integration of AI- driven solutions into fitness training, catering to individu

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Suwabe, Ryota, Takeshi Saito, and Toyohiro Hamaguchi. "Verification of Criterion-Related Validity for Developing a Markerless Hand Tracking Device." Biomimetics 9, no. 7 (2024): 400. http://dx.doi.org/10.3390/biomimetics9070400.

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Physicians, physical therapists, and occupational therapists have traditionally assessed hand motor function in hemiplegic patients but often struggle to evaluate complex hand movements. To address this issue, in 2019, we developed Fahrenheit, a device and algorithm that uses infrared camera image processing to estimate hand paralysis. However, due to Fahrenheit’s dependency on specialized equipment, we conceived a simpler solution: developing a smartphone app that integrates MediaPipe. The objective of this study was to measure hand movements in stroke patients using both MediaPipe and Fahren

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Nand, Rugwed. "Personalized Gym Trainer Using Mediapipe." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 1250–54. http://dx.doi.org/10.22214/ijraset.2023.57569.

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Abstract: With the growing demand in personalised fitness experiences, this paper takes a fresh look at home workouts by creating a Personalised Gym Trainer with the Mediapipe library. This sophisticated device combines pose detection technology with voice assistance to provide users with real-time feedback and personalised instruction while exercising. The system identifies various exercises accurately and leverages torso point detection for greater precision by leveraging the capabilities of the Mediapipe library and OpenCV for camera tasks. Individual Python modules for certain workouts suc

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S M, Dikshith. "AirCanvas using OpenCV and MediaPipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 14671–1473. https://doi.org/10.22214/ijraset.2025.66601.

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Human-Computer Interaction (HCI) has undergone significant transformations with the advent of Artificial Intelligence (AI) and Machine Learning (ML), enhancing the ways in which users engage with computing systems. This paper introduces AirCanvas, a novel hands-free digital interaction tool that leverages air gestures for intuitive and seamless computer control. The system uses advanced image processing techniques, specifically OpenCV for visual data analysis and MediaPipe for accurate hand gesture recognition, enabling users to manipulate virtual environments without physical touch. By integr

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KAVIYA,, D. "Open CV Based Hand Gesture Recognition for Virtual Keyboard Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42134.

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In the evolving landscape of computer technology, the interaction between humans and machines has undergone significant transformation, particularly emphasizing inclusivity and accessibility for individuals with disabilities. This project introduces a hand gesture recognition system designed to enable disabled individuals to interact with computers effortlessly, replacing traditional hardware like keyboards . Leveraging advanced technologies such as OpenCV, MediaPipe, and Python, the system allows users to control a virtual keyboard through intuitive hand gestures. By using a camera to track a

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USHA, M. NITHIN GOWDA H. SANTHOSH M. NANDA KUMAR S. NUTHAN PAWAR E. "REAL-TIME HAND SIGN TRAINING AND DETECTION." International Journal For Technological Research In Engineering 11, no. 5 (2024): 149–51. https://doi.org/10.5281/zenodo.10554229.

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Real-time hand sign recognition and detection are major for applications in human computer interaction, sign language interpretation, and gesture-based control systems.This project focuses on creating realtime hand gesture and finger gesture annotations using the MediaPipe framework in Python.The  hand gestures and finger gestures using keypoints and finger coordinates found by the MediaPipe framework.The system offers two machine learning models: one for recognizing hand signs and another for detecting finger gestures. It provides resources, including sample programs, model files, and tr

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Vaishnavi D. A., Lavanya, Anil Kumar C., Harish S., and Divya M. L. "MediaPipe to Recognise the Hand Gestures." WSEAS TRANSACTIONS ON SIGNAL PROCESSING 18 (July 2, 2022): 134–39. http://dx.doi.org/10.37394/232014.2022.18.19.

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Human Computer Interaction (HCI) can be improved drastically using the hand gesture based recognition system. This system is designed to detect the gestures of the hands in the images captured in real time. There are certain areas of intersect in the hands that are there for the classification. The gaming devices like Xbox, PS4 and smart phones are also using this method to solve few problems. In this paper a smart method i9s developed to solve the problem. Using Python 3.9 and MediaPipe, the hand gestures are recognised in the real-time images. The background subtraction is the key method use

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Yasumuro, Masanao, and Kenya Jin'no. "Japanese fingerspelling identification by using MediaPipe." Nonlinear Theory and Its Applications, IEICE 13, no. 2 (2022): 288–93. http://dx.doi.org/10.1587/nolta.13.288.

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R.Vasavi, Rahul Nenavath, Snigdha A, Jeffery Moses K., and Simha S.Vishal. "Painting with Hand Gestures using MediaPipe." International Journal of Innovative Science and Research Technology 7, no. 12 (2023): 1285–91. https://doi.org/10.5281/zenodo.7514430.

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The main objective of this project is that the hand gesture recognition can also be utilised in applications including industrial automation control, sign language interpretation, and rehabilitation equipment for individuals with physical disabilities of the upper extremities. And it is also find applications in varied domains like virtual environments, medical systems, smart surveillance etc. Hand gesture recognition is most significant for human-computer interaction. Gesture Recognition is a technique that uses mathematical algorithms to recognise human gestures. Gesture recognition identifi

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P, Dheeraj. "Landmark-based Dataset Generation using Mediapipe." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 90–92. https://doi.org/10.22214/ijraset.2025.68160.

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This paper focuses on creating structured landmarks-based feature extraction using MediPpipe. MediaPipe is an opensource framework for building pipelines to perform computer vision inference over arbitrary sensory data such as video or audio. Hand and facial expression recognition play a significant role in various domains like Human-computer interaction, assistive technology and emotion analysis. Traditional datasets primarily rely on raw images, which pose challenges in terms of computational complexity and privacy concerns. This paper represents a alternative approach for dataset creation b

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Huan, Yan, and Weiqi Yan. "Semaphore Recognition Using Deep Learning." Electronics 14, no. 2 (2025): 286. https://doi.org/10.3390/electronics14020286.

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This study explored the application of deep learning models for signal flag recognition, comparing YOLO11 with basic CNN, ResNet18, and DenseNet121. Experimental results demonstrated that YOLO11 outperformed the other models, achieving superior performance across all common evaluation metrics. The confusion matrix further confirmed that YOLO11 exhibited the highest classification accuracy among the tested models. Moreover, by integrating MediaPipe’s human posture data with image data to create multimodal inputs for training, it was observed that the posture data significantly enhanced the mode

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Badri, Fawaidul, Sulistya Umie Ruhmana Sari, and Shipun Anuar Bin Hamzah. "Analysis of Driver Drowsiness Detection System Based on Landmarks and MediaPipe." Inform : Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi 10, no. 1 (2025): 21–28. https://doi.org/10.25139/inform.v10i1.9325.

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Driver drowsiness is one of the leading causes of traffic accidents, especially during long-distance journeys. This study developed a detection system based on landmarks and the MediaPipe framework to analyze drowsiness through eye blink duration. The system employs coordinate point initialization using regression trees to accurately detect objects, such as eyes. The research data consists of 30 videos, each lasting 30 seconds, collected from four Trans Java bus drivers. The videos were extracted to identify facial detection histograms and analyzed based on eye blink duration. The testing resu

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Wagh, Vaidehi, Matthew W. Scott, and Sarah N. Kraeutner. "Quantifying Similarities Between MediaPipe and a Known Standard to Address Issues in Tracking 2D Upper Limb Trajectories: Proof of Concept Study." JMIR Formative Research 8 (December 17, 2024): e56682-e56682. https://doi.org/10.2196/56682.

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Abstract Background Markerless motion tracking methods have promise for use in a range of domains, including clinical settings where traditional marker-based systems for human pose estimation are not feasible. Artificial intelligence (AI)–based systems can offer a markerless, lightweight approach to motion capture. However, the accuracy of such systems, such as MediaPipe, for tracking fine upper limb movements involving the hand has not been explored. Objective The aim of this study is to evaluate the 2D accuracy of MediaPipe against a known standard. Methods Participants (N=10) performed a to

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Reddy, Cherukupally Karunakar, Suraj Janjirala, and Kevulothu Bhanu Prakash. "Gesture Controlled Virtual Mouse with the Support of Voice Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 2314–20. http://dx.doi.org/10.22214/ijraset.2022.44323.

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Abstract: This work offers a cursor control system that utilises a web cam to capture human movements and a voice assistant to quickly traverse system controls. Using MediaPipe, the system will let the user to navigate the computer cursor with their hand motions. It will use various hand motions to conduct activities such as left click and dragging. It also allows you to choose numerous items, adjust the volume, and adjust the brightness. MediaPipe, OpenCV etc advanced libraries in python are used to build the system. A hand gesture and a voice assistant used to physically control all i/o oper

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(Asst. Prof), E. Sakthivel. "Smart Body Posture Recognition and Guiding System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28058.

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In an era prioritizing health and wellness, the integration of the Smart Body Posture Recognition & Guiding System into self- service health kiosks signifies a paradigm shift in healthcare management. This groundbreaking research project leverages cutting-edge MediaPipe Body Tracking technology to revolutionize health monitoring capabilities. At its core, MediaPipe Body Tracking enables precise real-time identification and tracking of human body key points and movements. This breakthrough technology ensures unparalleled accuracy in health parameter measurements within the kiosk interface.

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JAGAN M, Mr. "AI - Enhanced Nighttime Seizure Surveillance." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45591.

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Abstract – Nighttime seizures pose serious health risks to individuals with epilepsy, often going undetected until critical complications occur. This system tackles these challenges by using advanced computer vision and AI to create a non-invasive, real-time seizure detection solution. It integrates technologies like MediaPipe, OpenCV, and edge computing to analyze video streams and detect seizure-specific patterns through body pose estimations and physiological inputs. High-resolution video feeds, enhanced by infrared capabilities, enable effective nighttime surveillance. MediaPipe's Pose Est

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Melvin, Rivin Jose. "Robot Arm Control Using Hand Gesture." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42695.

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- T Robotic arm control has traditionally relied on mechanical input methods such as joysticks or coding commands. However, gesture recognition provides an intuitive alternative, especially in environments where direct control might be challenging. This paper explores the use of Mediapipe for gesture recognition to control robotic arm movements in real-time. By integrating gesture recognition with robotic systems, we can enhance user interaction, making robotic arms more accessible for users without extensive training. Key Words: Gesture Recognition, Robotic Arm Control, Mediapipe, ROS, MoveIt

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Agustin, Risda Rosdiana, Hendra Maulana, and Eka Prakarsa Mandyartha. "DETECTION OF ACTIONS BISINDO (INDONESIAN SIGN LANGUAGE) INTO TEXT-TO-SPEECH USING LONG SHORT-TERM MEMORY WITH MEDIAPIPE HOLISTICS." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2023): 1051–61. https://doi.org/10.52436/1.jutif.2024.5.4.1492.

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Sign language is frequently used by those who have difficulty hearing or speaking to communicate. Because it is a non-verbal language that expresses meaning through hand and body gestures, sign language is an essential form of communication for people who rely on it. The objective of this work is to develop a detection that can understand actions made in Indonesian Sign Language (BISINDO), translate them into text, and use speech recognition (Text- to-Speech) to provide audio responses. In particular at Sekolah Luar Biasa, the main objective is to assist and enhance communication among persons

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D, Neha, and Dr S. K. Manju Bargavi. "Virtual Fitness Trainer using Artificial Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (2023): 1499–507. http://dx.doi.org/10.22214/ijraset.2023.49718.

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Abstract: An AI fitness trainer is a computer application that utilizes the capabilities of Python, OpenCV, and MediaPipe to guide users through physical fitness routines. The application uses computer vision techniques provided by OpenCV to track the user's movements and provide feedback on form and technique. MediaPipe is used to process the video data and provide realtime analysis. The application also utilizes machine learning algorithms to provide personalized fitness recommendations and progress tracking. The combination of these technologies provides a highly interactive and effective w

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Madhira, Aditya. "Computer Automation Using Gesture Recognition and Mediapipe." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (2022): 3014–18. http://dx.doi.org/10.22214/ijraset.2022.44542.

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Abstract: Automation is the use of technology to accomplish a task with as little human interaction as possible. In computing, automation is usually accomplished by a program, a script, or batch processing. Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures. Automation of tasks can be achieved with the help of “Gestures”. Using Gestures to interact with the computer is a way of achieving Human Computer Interaction with less utilization of physical devices. Our system consists of four phases: Facial Authentication, Hand Tracki

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S, Dr Nandagopal, Soundarya R, Vaishnavi S, Vanisri S, and Hiranya S. "AI Virtual Painter using OpenCV and Mediapipe." International Journal of Engineering Research in Computer Science and Engineering 9, no. 11 (2022): 13–16. http://dx.doi.org/10.36647/ijercse/09.11.art004.

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Recognition of hand gestures has great importance for Human-computer interaction (HCI). The human hand is very small with complex junctions compared with the entire human body, hence recognizing the human hand is not an easy task. By using the hand gesture recognition the hand point/coordinates of hands can be detected using which we can make many impossible happens. Our work indicates one such finding, that is, VIRTUAL PAINTER. In our project, the main objective is to display the words on the monitor screen which we write on air in front of the webcam. This is done by recognizing the human ha

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Dutta, Chinmayee, and Ritu Maity. "Intelligent door unlock system using AI MediaPipe." International Journal of Data Informatics and Intelligent Computing 3, no. 1 (2024): 36–43. http://dx.doi.org/10.59461/ijdiic.v3i1.96.

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Prioritizing advanced security, the infusion of AI Mediapipe into the realm of smart door access not only simplifies access control but also guarantees unmatched protection, reshaping the expectation for safety and reliability. Implementing AI Mediapipe facilitates real-time gesture recognition, face detection, and hand tracking, optimizing access control in smart door systems. Integrating software and hardware set a new standard in the domain of smart door access. Here, we have proposed a smart system with a high-resolution camera to capture faces and connect them to a processing unit, i.e.,

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Jlidi, Nozha, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika. "MediaPipe with GNN for Human Activity Recognition." JUCS - Journal of Universal Computer Science 30, no. 6 (2024): 791–813. http://dx.doi.org/10.3897/jucs.111676.

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Human interaction and computer vision converge in the realm of Human Activity Recognition (HAR), which is a research field dedicated to the creation of automated systems capable of observing and categorizing human activities. This domain closely aligns with machine learning, involving the development of algorithms and models adept at learning to recognize and classify patterns within data. HAR typically unfolds in two pivotal phases: data acquisition and processing, followed by activity classification. In the initial phase of data acquisition and processing, information is gathered from variou

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Tran, Linh, and Thai Hoang Huynh. "Automate Fall Detection Using MediaPipe Keypoint-extraction." International Journal of Computer Applications 186, no. 80 (2025): 34–39. https://doi.org/10.5120/ijca2025924733.

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Praneetha, K. M. S. V. "Sign Language Detection Using Mediapipe and ML." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 6710–13. https://doi.org/10.22214/ijraset.2025.69945.

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Sign language recognition systems are crucial for improving communication between the hearing and deaf communities. This paper explores the development of a real-time sign language detection system that uses a combination of computer vision techniques and machine learning algorithms. Specifically, it employs MediaPipe, a computer vision framework, to extract hand landmarks, and a Random Forest Classifier to classify the gestures. This system is capable of recognizing ten distinct signs in real time. The paper provides a detailed description of the design, development, and implementation of the

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Jlidi, Nozha, Sameh Kouni, Olfa Jemai, and Tahani Bouchrika. "MediaPipe with GNN for Human Activity Recognition." JUCS - Journal of Universal Computer Science 30, no. (6) (2024): 791–813. https://doi.org/10.3897/jucs.111676.

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Human interaction and computer vision converge in the realm of Human Activity Recognition (HAR), which is a research field dedicated to the creation of automated systems capable of observing and categorizing human activities. This domain closely aligns with machine learning, involving the development of algorithms and models adept at learning to recognize and classify patterns within data. HAR typically unfolds in two pivotal phases: data acquisition and processing, followed by activity classification. In the initial phase of data acquisition and processing, information is gathered from variou

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Putra, Ichsan Arsyi, Oky Dwi Nurhayati, and Dania Eridani. "Human Action Recognition (HAR) Classification Using MediaPipe and Long Short-Term Memory (LSTM)." TEKNIK 43, no. 2 (2022): 190–201. http://dx.doi.org/10.14710/teknik.v43i2.46439.

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Human Action Recognition is an important research topic in Machine Learning and Computer Vision domains. One of the proposed methods is a combination of MediaPipe library and Long Short-Term Memory concerning the testing accuracy and training duration as indicators to evaluate the model performance. This research tried to adapt proposed LSTM models to implement HAR with image features extracted by MediaPipe library. There would be a comparison between LSTM models based on their testing accuracy and training duration. This research was conducted under OSEMN methods (Obtain, Scrub, Explore, Mode

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Mhaiskar, Rutuja, Vaithiyanathan Dhandapani, Preeti Verma, and Baljit Kaur. "Performance Analysis of Human Activity." ITM Web of Conferences 56 (2023): 05006. http://dx.doi.org/10.1051/itmconf/20235605006.

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This project aims to develop an AI-powered gym assistant using Jupyter Notebook and MediaPipe, a popular computer vision library, to count the repetitions of three joint exercises: curls, squats, and sit-ups. The system will provide real-time feedback and monitoring, allowing users to track their progress and improve performance. The proposed method utilizes MediaPipe, which offers pre-trained machine-learning models for human pose estimation and hand tracking. These models will accurately detect and track critical body joints and hand movements during the exercises. The system will then analy

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Tanjaya, Kenneth Angelo, Mohammad Farid Naufal, and Heru Arwoko. "Pilates Pose Classification Using MediaPipe and Convolutional Neural Networks with Transfer Learning." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 2 (2023): 212–22. https://doi.org/10.26555/jiteki.v9i2.25975.

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A sedentary lifestyle can lead to heart disease, cancer, and type 2 diabetes. An anaerobic exercise called pilates can address these problems. Although pilates training can provide health benefits, the heavy load of pilates poses may cause severe muscle injury if not done properly. Surveys have found that many teenagers are unaware of the movements in pilates poses. Therefore, a system is needed to help users classify pilates poses accurately. MediaPipe is a system that accurately extracts the real time human body skeleton. Convolutional Neural Network (CNN) with transfer learning is an accura

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Parikh, Shrey. "Leveraging Python and Mediapipe for Hand and Body Tracking: A Comprehensive Review." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 1771–72. http://dx.doi.org/10.22214/ijraset.2024.59134.

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Abstract: This research paper provides a comprehensive review of hand and body tracking techniques using Python and Mediapipe, with a particular focus on their applications in interactive systems and gaming. Hand and body tracking have become essential components in various fields, including augmented reality (AR), virtual reality (VR), human-computer interaction (HCI), and gaming. Leveraging Python and the Mediapipe library, this study explores the underlying principles, methodologies, and advancements in hand and body tracking algorithms. Additionally, it discusses the potential impact of th

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Hetvi Gunjan Shah, Vraj Bhavesh Suthar, Shital P. Thakkar, and Vinay M. Thumar. "Real-time Performance Comparison of Face Detection Algorithms using Raspberry Pi." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 10 (2024): 2440–45. http://dx.doi.org/10.47392/irjaeh.2024.0334.

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Abstract This study reviewed state-of-the-art face-detection techniques like Haar cascade, Dlib HOG, MTCNN, and MediaPipe; implemented and tested them on Raspberry Pi, and evaluated their accuracy, speed, and frames per second. Overall, the research underscores the practical challenges of face detection, including varying lighting, facial expressions, occlusions, poses, scale of face, and accessories, and provides valuable insights for developers and researchers working on edge AI applications on low-cost edge devices. The study found that the MediaPipe face detection algorithm demonstrated ro

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Lei, Miaowen, Zuxuan Wang, and Fang Chen. "Ballet Form Training Based on MediaPipe Body Posture Monitoring." Journal of Physics: Conference Series 2637, no. 1 (2023): 012019. http://dx.doi.org/10.1088/1742-6596/2637/1/012019.

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Abstract People are increasingly turning to the cloud in the context of “healthy China” to engage in online exercise. The use of artificial intelligence technology to address broad population health-related challenges has become increasingly important as information technology has matured. The MediaPipe artificial intelligence framework, which Google recently released, is used in this article to optimize video feedback and support the “cloud movement” of widespread home ballet instruction in order to examine the effects of digital technology-enabled ballet training on the general improvement o

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Afifah, A. Najiah Nurul, and Andi Asvin Mahersatillah Suradi. "Sistem Deteksi Postur Duduk Berbasis MediaPipe untuk Meningkatkan Ergonomi dan Kesehatan Pekerja." SISITI : Seminar Ilmiah Sistem Informasi dan Teknologi Informasi 14, no. 1 (2025): 168–74. https://doi.org/10.36774/sisiti.v14i1.1690.

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Postur duduk yang tidak ergonomis dapat menyebabkan berbagai masalah kesehatan, seperti nyeri punggung, gangguan muskuloskeletal, dan penurunan produktivitas. Penelitian ini bertujuan untuk mengembangkan sistem deteksi postur duduk secara real-time menggunakan MediaPipe Pose untuk menganalisis postur tubuh berdasarkan landmark tubuh, seperti bahu, pinggul, dan lutut. Tujuannya adalah mengidentifikasi apakah postur responden termasuk ergonomis atau tidak. Metode yang digunakan melibatkan pengolahan video, di mana MediaPipe Pose mendeteksi posisi landmark tubuh, diikuti dengan perhitungan sudut

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Benitez-Garcia, Gibran, Jesus Olivares-Mercado, Gabriel Sanchez-Perez, and Hiroki Takahashi. "IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition." Applied Sciences 15, no. 11 (2025): 6321. https://doi.org/10.3390/app15116321.

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Hand gesture recognition (HGR) heavily relies on high-quality annotated datasets. However, annotating hand landmarks in video sequences is a time-intensive challenge. In this work, we introduce IPN HandS, an enhanced version of our IPN Hand dataset, which now includes approximately 700,000 hand skeleton annotations and corrected gesture boundaries. To generate these annotations efficiently, we propose a novel annotation tool that combines automatic detection, inter-frame interpolation, copy–paste capabilities, and manual refinement. This tool significantly reduces annotation time from 70 min t

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Mohd Dhuzuki, Nurul Hanis, Ahmad Anwar Zainuddin, Nur Anis Sofea Kamarul Zaman, et al. "Design and Implementation of a Deep Learning-Based Hand Gesture Recognition System for Rehabilitation Internet-of-Things (RIoT) Environments Using MediaPipe." IIUM Engineering Journal 26, no. 1 (2025): 353–72. https://doi.org/10.31436/iiumej.v26i1.3455.

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Frequent hospital visits for hand rehabilitation exercises, such as strengthening and opposition exercises, present significant challenges, especially for patients in remote areas. This paper addresses this problem by developing a Rehabilitation Internet-of-Things (RIOT) system that utilizes MediaPipe with its pre-trained Deep Learning (DL) to deliver real-time feedback during hand rehabilitation exercises alongside Web Assembly (WASM) for efficient processing. The system's objective is to provide precise, real-time tracking of hand movements, enabling patients to perform exercises at home by

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Pathan, Yusuf. "Hand Gesture-Control Gaming and Mouse Navigation System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41237.

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-- This paper explores the development of a gesture-controlled system that enables users to interact with computer games, such as Hill Climb Racing, using hand movements. The system utilizes real-time data from a webcam, along with libraries like MediaPipe, OpenCV, and Pygame, to detect and interpret hand gestures. By tracking hand movements, the software allows users to control game functions and navigate the mouse pointer without the need for traditional input devices. This approach not only enhances the gaming experience but also promotes hands- free interaction, with potential for future i

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Amanda Muchsin Chalik, Bilal Abdul Qowy, Faiz Hanafi, and Ahlijati Nuraminah. "Mouse Tracking Tangan dengan Klasifikasi Gestur Menggunakan OpenCV dan Mediapipe." Jurnal Ilmiah Teknik Informatika dan Komunikasi 1, no. 2 (2021): 10–18. http://dx.doi.org/10.55606/juitik.v1i2.323.

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Pengenalan gerakan tangan manusia merupakan bidang yang banyak diteliti saat ini, karena pendeteksian dan pengenalan gerakan tangan memiliki potensi besar untuk digunakan sebagai cara berinteraksi kepada komputer dan mengendalikannya di masa depan. Pergerakan tangan sangatlah berguna untuk saat ini, karena serangan covid – 19 yang terus melanda dunia , mengharuskan kita untuk tidak menyentuh banyak peralatan yang ada di tempat umum , dan mengharuskan kita untuk tidak berkontak langsung dengan benda ataupun dengan manusia di sekitar kita. Penelitian kali ini menggunakan package dari bahasa pemr

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P., Swetha, and Sucharitha K. "Sign Language Recognition Utilizing LSTM & Media pipe for Dynamic Gestures of ISL." IJRSET JUNE Volume 10 Issue 6 10, no. 6 (2023): 1–6. https://doi.org/10.5281/zenodo.8382974.

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Humans, in general, are social creatures who communicate themselves through an assortment of spoken languages. Deaf and Mute individuals converse in a manner that's comparable, however many others areignorantoftheirsignlanguage. Asaresult, thereisaneedtodevelopasystemthatfacilitatescommunication among the hearing and hard-of-hearing communities. This research offers a real-time Indian Sign Language (ISL) recognition system for 24 dynamic signals using the Mediapipe framework and an LSTM network. The method proposed in the study involves training a LSTM to differentiate between different si

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Leer, Alexandra, Beatriz Garcia Santa Cruz, Frank Hertel, Klaus Peter Koch, and Rene Peter Bremm. "Design of an experimental platform of gait analysis with ActiSense and StereoPi." Current Directions in Biomedical Engineering 8, no. 2 (2022): 572–75. http://dx.doi.org/10.1515/cdbme-2022-1146.

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Abstract Gait analysis is a systematic study of human movement. Combining wearable foot pressure sensors and machine learning (ML) solutions for a high-fidelity body pose tracking from RGB video frames could reveal more insights into gait abnormalities. However, accurate detection of heel strike (HS) and toe-off (TO) events is crucial to compute interpretable gait parameters. In this work, we present an experimental platform to study the timing of gait events using a new wearable foot pressure sensor (ActiSense System, IEE S.A., Luxembourg), and Google’s open-source ML solution MediaPipe Pose.

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Maryamah, Maryamah, Muhammad Alfian Pratama, Muhammad Reza Erfit, Nadiya Mujahidatul Farhani, and Ignatius Arvantya Hartono. "Klasifikasi Abjad SIBI (Sistem Bahasa Isyarat Indonesia) menggunakan Mediapipe dengan Metode Deep Learning." PROSIDING SEMINAR NASIONAL SAINS DATA 3, no. 1 (2023): 134–41. http://dx.doi.org/10.33005/senada.v3i1.102.

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General public knowledge in Indonesia regarding the Indonesian Sign Language System (SIBI) is quite low. This can prevent deaf and mute people from doing activities in public facilities. In this paper, we propose an alphabetical classification of SIBI sign language using the mediapipe and the Deep Learning method to help deaf and mute people communicate with the public. The methodology of this paper collects a dataset in the form of image data from the hand patterns of each SIBI sign language alphabet by combining a webcam with the help of the open-cv library to retrieve image data. With the m

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Lafayette, Thiago Buarque de Gusmão, Victor Hugo de Lima Kunst, Pedro Vanderlei de Sousa Melo, et al. "Validation of Angle Estimation Based on Body Tracking Data from RGB-D and RGB Cameras for Biomechanical Assessment." Sensors 23, no. 1 (2022): 3. http://dx.doi.org/10.3390/s23010003.

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Motion analysis is an area with several applications for health, sports, and entertainment. The high cost of state-of-the-art equipment in the health field makes it unfeasible to apply this technique in the clinics’ routines. In this vein, RGB-D and RGB equipment, which have joint tracking tools, are tested with portable and low-cost solutions to enable computational motion analysis. The recent release of Google MediaPipe, a joint inference tracking technique that uses conventional RGB cameras, can be considered a milestone due to its ability to estimate depth coordinates in planar images. In

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Lupito, Vemas Nandra, Muchamad Malik, and Aan Burhanudin. "Pengembangan Sistem Kendali Lengan Robot Humanoid Berbasis Pengolahan Citra Real-Time Menggunakan MediaPipe." JURNAL CRANKSHAFT 8, no. 2 (2025): 112–23. https://doi.org/10.24176/cra.v8i2.15117.

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Perkembangan teknologi pengenalan gerakan manusia telah mendorong inovasi dalam pengendalian robot humanoid berbasis visi komputer. Penelitian ini bertujuan untuk mengimplementasikan sistem kendali lengan robot humanoid menggunakan framework MediaPipe berbasis Python yang diintegrasikan dengan kamera laptop sebagai sensor utama. Sistem ini dirancang untuk meniru gerakan tangan manusia secara real-time, dengan memanfaatkan data landmark pose tubuh yang dihasilkan oleh MediaPipe Pose Tracking. Koordinat titik-titik tubuh (bahu, siku, pergelangan tangan) dikalkulasikan menjadi sudut artikulasi me

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Sruthi S and Swetha S. "Hand Gesture Controlled Presentation using OpenCV and MediaPipe." international journal of engineering technology and management sciences 7, no. 4 (2023): 338–42. http://dx.doi.org/10.46647/ijetms.2023.v07i04.046.

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In today's digital era, presentations play a crucial role in various domains, ranging from education to business. However, traditional manual presentation methods, reliant on input devices such as keyboards or clickers, have inherent limitations in terms of mobility, interactivity, and user experience. To address these limitations, gesture-controlled presentations have emerged as a promising solution, harnessing the power of computer vision techniques to interpret hand gestures and enable natural interaction with presentation content. This paper presents a comprehensive system for hand gesture

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Rasyid, Muhammad Furqan, Muhammad Syukri Mustafa, Andi Asvin Mahersatillah Suradi, Muhammad Rizal, Mushaf Mushaf, and Arham Arifin. "Deteksi Mata di Video Smartphone Menggunakan Mediapipe Python." JOINTECS (Journal of Information Technology and Computer Science) 8, no. 2 (2023): 49. http://dx.doi.org/10.31328/jointecs.v8i2.4562.

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Sharma, Abhishant, and Dev Baloni. "Predict The Caloric Expenditure and Pose Estimation Through the Assistance of The Virtual Gym Coach." International Journal of Membrane Science and Technology 10, no. 1 (2023): 1823–32. http://dx.doi.org/10.15379/ijmst.v10i1.3440.

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This research paper introduces a novel virtual gym assistant leveraging Google's Mediapipe library, designed for diverse multimodal machine learning and deep learning pipelines. The system offers real-time guidance by analyzing user movements during specific exercises using posture estimation algorithms. Developed with Mediapipe's deep algorithms and pose estimation module, the system captures user movements through the identification of body landmarks, facilitating rep counting for each exercise. Angles and landmarks are then processed and transmitted to various machine learning models, enabl

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Erni Wigati. "REALTIME IMPLEMENTATION OF SHOULDER POSTURE DETECTION WITH COMPUTER VISION." International Journal Science and Technology 3, no. 2 (2024): 12–16. http://dx.doi.org/10.56127/ijst.v3i2.1479.

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The development of health technology is currently growing rapidly along with the needs of health workers for medical information. Computer Vision is one of the technologies used in the medical world, including the human shoulder posture detection system using the MediaPipe library. This system automatically calculates the logic of posture measurements based on the basis of manual measurements with anthropometric tools, so that health workers can easily get precise and accurate information about the results of shoulder posture detection. The system created with the Python programming language a

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B N, Rekha. "Gesture Controlled Virtual Mouse using AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2411–16. http://dx.doi.org/10.22214/ijraset.2023.52100.

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Abstract: This project offers a cursor controlsystem that quickly navigates system controls whileusing a voice assistant and a camera to record usermotions. Using the aid of MediaPipe, the user can control the computer cursor with hand gestures. It will perform actions like left clicking and draggingusing a variety of hand motions. Additionally, you have a choice to adjust the brightness, loudness, and a number of other things. The system is constructed using advanced Python packages like MediaPipe, OpenCV, etc. All i/o activities are physically controlled by a hand motion and a voiceassistanc

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Irviantina, Syanti, Dela Agustri Wijaya, Desiana R. Situmorang, and Nazhiifah Mawaddah Juliyanda Nasution. "Deteksi Bahasa Isyarat Berdasarkan Abjad Menggunakan Metode LSTM (Long Short Term Memory)." Majalah Ilmiah METHODA 14, no. 3 (2024): 371–76. https://doi.org/10.46880/methoda.vol14no3.pp371-376.

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The LSTM based sign language detection system combined with the use of mediapipe can recognize hand gestures in real time with high accuracy. Alphabet based sign language can use this model to collect temporal patterns of hand gestures. The data used in this study are 30 sample for each alphabet based on American Sign Language (ASL). The data is processed through landmark detection on the hand using mediapipe and opencv, keypoints extraction, folder creation and pre – processing with 80% data divisior for training data and 20 % data for testing data. Using Adam optimization, categorical crosse

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