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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial Measurement Unit 11

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

引用

11th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2024

作者： Chang, Tien-Chiao Wu, Yu-Chi Han, Chin-Chuan Chang, Chao-Shu National United University Dept. of Electrical Engineering Miao-Li Taiwan National United University Dept. of Computer Science and Information Engineering Miao-Li Taiwan National United University Dept. of Information Management Miao-Li Taiwan

ISBN: (纸本)9798350366501

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow's IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist's pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%. © 2024 IEEE.

关键词： Gesture recognition

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Real Time Monitoring and Configuration Interface for Smart Home Device Management Using Streamlit and Pygame

Real Time Monitoring and Configuration Interface for Smart H...

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Advances in Data engineering and Intelligent Computing Systems (ADICS), International Conference on

作者： G Suma Mani G Venkata Krishna M.D. Anto Praveena P. Asha Mercy Paul Selvan Dept. Of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

One of the helpful tool for measuring and managing home power consumption is one of the clever and creative tool for tracking the usage of Home Power Management System. The Goal of this system is to lower the power bills, also promote energy efficiency, and provide the users active control over how much energy they use. Smart meters, an intuitive and user-friendly smartphone app, and real-time energy monitoring sensors are some of the system's essential parts and these are the key parts of the system. Installing smart meters makes it possible to accurately track the power usage and provide data to the system. Users can be able to see their energy usage instantly and make smart decisions when real-time energy monitoring equipment is included. Further, the system uses advanced smart technology, analytics and machine learning algorithms to provide clients customized suggestions on how to save energy consumption. Homes may save a lot of money on electricity bills, help the environment, and have more control over how much energy they use by using our Smart Home Power Management System.

关键词： Renewable energy sources Home appliances Energy consumption Power system management Real-time systems Energy efficiency Smart meters

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Deep Feature Fusion Based Brain Tumor Classification From Imbalance MRI Dataset Using Weighted Loss Functions

Deep Feature Fusion Based Brain Tumor Classification From Im...

引用

Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Taieba Tasnim Farjana Parvin Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Deep learning using MRI images to classify brain tumours presents a significant challenge due to imbalanced training datasets, where one tumor type may have far more samples than others. The overrepresented class may perform worse in classifiers as a result of this imbalance. In order to tackle this issue, the paper examines how feature learning in CNNs is affected by weighted loss functions to manage data imbalance. The article propose deep feature fusion, where particular CNN model features that were trained using three distinct loss functions are fused and classified using softmax classifier. This approach lead to notable improvements in brain tumor classification compared to a CNN trained with standard cross-entropy loss. Notably, the proposed methods substantially reduce classification errors between uneven class samples. The proposed model achieved an impressive 94.5% classification accuracy, accompanied by an average Fl-score of 94.6%, average precision of 94.6 %, and average recall of 95%. These findings underscore the effectiveness of our methodology in improving brain tumor classification accuracy, offering a promising avenue for more reliable diagnosis and treatment planning in clinical settings,

关键词： Training Representation learning Adaptation models Accuracy Magnetic resonance imaging Brain tumors Brain modeling Planning Reliability Standards

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Duration-Based Investigation of User Content Choices in the Exit of Filter Bubbles

Duration-Based Investigation of User Content Choices in the ...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Rabiatu Bunza Dept. of Computer Science Faculty of Science and Engineering Swansea University United Kingdom

ISBN: (数字)9798350394634

ISBN: (纸本)9798350394641

Content personalization on social platforms has been linked to the creation of filter bubbles. The algorithms provide content recommendations based on the user’s browsing history and interests, which limits content diversity. Studies have been conducted on platforms such as Facebook and YouTube to investigate user awareness and strategies to exit filter bubbles. However, no study has employed a multi-platform, user-centered perspective in investigating filter bubble exit through change in search content, as well as the duration of the content transition across multiple platforms. This study examines the duration of user exit of filter bubbles, across three social platforms; TikTok, YouTube Shots, and Instagram Reels, as well as the impact of word similarity on the duration of the transition between contents, across the platforms. The methodology applied involved triggering the filter bubble effect through user interactions with the search content across the different platforms. Findings revealed that the mean time of a transition between search terms on TikTok is significantly higher than the mean time on Instagram reels, and YouTube shorts. Similarly, the study identified differences in the duration of transition across the different platforms, due to the similarity of the search terms. Apart from improving user awareness of a filter bubble exit strategy, this study contributes to Human-computer Interaction research by highlighting some design implications for future social media application design.

关键词： Human computer interaction Video on demand Social networking (online) Psychology Filtering algorithms Web sites Multimedia communication

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Context Aware Recommendation System Using LLM

Context Aware Recommendation System Using LLM

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Kagolanu Venkata Chandra Madhav Asritha Veeramaneni Vinnakota Sai Vivek Meena Belwal Sangita Khare Dept of Computer Science and Engineering Amrita School of computing Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

This paper proposes a unique approach to emotion recognition and employs Vision Transformer (ViT) model for the sake of giving personalised suggestions of music selections and environments. ViT is able to determine the user emotion based on his facial expression detection technology. Those emotions will provide the critical inputs that will allow recommendations to be modified to context-aware standards the mood you're in the time of year, the specifics of the month. Proprietary technology creates music playlists suitable to the mood of the moment as it suggests the best locations for experiences based on an emotional context and other external factors such as the season and time of month. As we move deeper into the future, this merging of emotion detection technology and customized recommendations can deliver an unparalleled user experience by offering a user exactly what they would like in their location of the area they are you engaged in. It just ensures that the location to suggest should not only be ideal to the user mood but must also be practical as well by taking into account the locations weather and local events. The whole concept of this is to create a more rounded and complete approach to curating a fitness routine tailored to the individual, taking into account their mental health as well as external issues.

关键词： Emotion recognition computer vision Mood Merging Mental health Transformers User experience Recommender systems Standards Meteorology

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Designing a Graphics Processing Unit for 2D Rendering on FPGA for Educational Purpose

Designing a Graphics Processing Unit for 2D Rendering on FPG...

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International Conference on Advancement in Electrical and Electronic engineering (ICAEEE)

作者： Rupak Saha Nahin Ul Sadad Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering.

关键词： Visualization Three-dimensional displays Heuristic algorithms Graphics processing units Signal processing algorithms computer architecture Rendering (computer graphics)

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Detecting and Eliminating Cardiac Artifact from Endovascular EEG Signals 46

Detecting and Eliminating Cardiac Artifact from Endovascular...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Sreekantham, Shreya Chetty, Nikole Weber, Douglas J. Carnegie Mellon University Dept. of Computer Science Pittsburgh United States Carnegie Mellon University Dept. of Mechanical Engineering Pittsburgh United States Carnegie Mellon University Dept. of Mechanical Engineering Neuroscience Institute Pittsburgh United States

ISBN: (纸本)9798350371499

Paralysis is a debilitating condition that affects more than 5.4 million people in the U.S. In severe cases, the paralyzed patient is incapable of communication. Restoring this communication is a primary goal of caretakers and is critical to improving the patient's quality of life. Brain-computer interfaces (BCIs) that directly access signals from the motor cortex are a promising method of circumventing the condition causing paralysis, typically using machine learning (ML) to predict motor intent from brain signals. However, BCIs are highly invasive and subjects have primarily been limited to patients with mild to moderate paralysis. The Stentrode is a novel technology that records electroencephalographic (EEG) signals via an electrode array placed endovascularly in the superior sagittal sinus. The first clinical trials of this technology aim to enable digital communication for severely paralyzed patients, translating brain signals from attempted movements into computer control inputs like mouse clicks. However, recordings of EEG are often contaminated with artifacts, including biopotentials arising from other excitable tissues, such as the heart and skeletal muscle. This study characterizes the electrocardiographic (ECG) artifact detected in the Stentrode recordings and proposes an automated Independent Component Analysis (ICA) method for removing this artifact. We compare the effectiveness of this method to previous methods for removal. Quantifying and eliminating the cardiac artifact is critical to accurately decode signals from the motor cortex and restore patients' ability to communicate. © 2024 IEEE.

关键词： Brain computer interface

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Revolutionizing Road Safety for Advanced Vehicular Accident Prediction

Revolutionizing Road Safety for Advanced Vehicular Accident ...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： T. Deepika N. Yuvaraj K.R. Sri Preethaa Dept. of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

The detection and tracking of vehicle speed are crucial for ensuring the safety of civilians and preventing accidents. Speed tracking plays a pivotal role in traffic surveillance, where effective management and the well-being of citizens take precedence. The approach leverages computer vision-based models, specifically Multi-scale Detector for object detection, and a fusion of the DeepSORT and Centroid Sort algorithms for vehicle tracking from streaming video. To cater to the requirements of these algorithms, a custom dataset was developed within a controlled setting. Dataset augmentation was employed to prevent overfitting, thereby enhancing the dataset's diversity and the model's ability to generalize. The method demonstrates the capacity to accurately detect and track vehicles, maintaining performance even in high-traffic conditions. Cross-validation at every epoch during the training phase was implemented to increase the model's accuracy. In addition to the technical aspects, an LED display board was integrated to provide real-time data to oncoming vehicles at road curves. This display showcases information such as vehicle types, passage time, and the number of vehicles within the curvy road. The proposed Multi-Scale Detector model, a customized version of the You Only Look Once (YOLO) architecture, achieved a mean average precision (mAP) of 0.954 at a confidence interval of 0.5. This overall research provides an effective and accurate solution for optimizing vehicle tracking, enhancing transportation safety, and enabling a variety of practical applications in traffic management and monitoring.

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Deep Learning Techniques for Speech Emotion Recognition

Deep Learning Techniques for Speech Emotion Recognition

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International Conference on Futuristic Technologies (INCOFT)

作者： Bhavani. R T Vijay Muni Ravi Kumar Tata Jonnadula Narasimharao K Murali Harpreet Kaur Dept.of Information Technology Kings Engineering College Chennai India Dept.of Electrical and Electronics Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Dept.of Computer Science and Engineering CMR Technical Campus Medchal Hyderabad India Dept.of Electronics and Communication Engineering VijayaInstitute of Technology for Women Enikepadu India Dept.of Computer Science and Engineering Lovely Professional University Phagwara India

The identification of feelings sent via one’s voice has evolved from a niche field into an essential part of humancomputer interaction (HCI). These systems seek to facilitate more natural interactions between humans and machines by favouring direct voice interaction over the use of conventional input devices in order to comprehend verbal information and to make it simpler for human listeners to respond. A few examples include the use of emotion patterns derived from speech in medical applications, dialogue systems for spoken languages such as those used in cell phone call centres, onboard driving systems in automobiles, and the usage of such patterns in other contexts. In the research that has been done on speech emotion recognition (SER), a variety of methods, many of which are considered to be industry standards in speech analysis and classification, have been applied to the process of extracting emotions from signals. The most crucial stages of the speech emotion recognition (SER) process are the phases that deal with the extraction of features and the categorization of features. For the purpose of speech processing, researchers have developed a variety of features, including prosodic features, vocal traction features, and other hybrid features. The second step involves the categorization of features through the application of deep learning methods. These methods have just lately been put up as a potential replacement for SER’s more conventional methods.

关键词： Deep learning Industries Emotion recognition Speech analysis Input devices Medical services Feature extraction

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Human Emotion Recognition from EEG-brain Signals using Enhanced Machine Learning Method

Human Emotion Recognition from EEG-brain Signals using Enhan...

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International Conference on Advancements in Computational sciences (ICACS)

作者： Ghulam Ghous Shaheryar Najam Ahmad Jalal Dept. of Electrical & Computer Engineering Riphah International University Islamabad Pakistan Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (数字)9798331533038

ISBN: (纸本)9798331533045

This study explores the application of advanced machine learning techniques to EEG data for detecting emotions in individuals with cognitive disabilities. Utilizing the SEED-IV dataset, we analyze EEG recordings from 15 participants across multiple sessions to identify emotional states such as happiness, sadness, fear, and neutrality. To ensure data quality, we employ preprocessing techniques including bandpass filtering and downsampling. Our proposed model integrates multi-class Support Vector Machine (SVM) with AAFST, an innovative feature selection and transformation mechanism. The effectiveness of this approach is demonstrated by an SVM accuracy of 85%, showcasing its capability to extract subtle emotional responses from EEG data. This research contributes to the growing field of affective computing, emphasizing the advantages of combining machine learning with EEG analysis to enhance the detection and understanding of emotions in individuals with cognitive disabilities.

关键词： Support vector machines Emotion recognition Accuracy Filtering Machine learning Feature extraction Electroencephalography Physiology Recording Emotional responses

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