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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit Technology University of Phayao School of Information and Communication Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Numerous research studies on human activity recognition (HAR) have utilized smartphone sensors, such as accelerometers, gyroscopes, and magnetometers. The accelerometer, in particular, has been considerably explored as the primary sensor in HAR research. Recently, researchers have integrated different smartphone sensors to enhance recognition interpretation. Nevertheless, it is still challenging to determine the optimal usage of these sensors, either individually or in combination, for better identification effectiveness in various motion situations. In this article, we explore how different motion sensors behave during the activity recognition process, using various deep learning (DL) approaches to classify physical actions based on smartphone sensor data. We evaluate the DL models using a publicly benchmarked dataset of data collected from ten participants performing eight movements while carrying smartphones in different situations. Our experimental results reveal that, except for the magnetometer, each sensor can play a leading role in HAR, depending on the recognized activity, body position, data features employed, and classification approach. Additionally, we find that the combination of sensors only improves overall recognition interpretation when their individual performances are low. © 2023 IEEE.

关键词： Smartphones

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Efficiency Effect of Obstacle Margin on Line-of-Sight in Wireless Networks

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computers, Materials & Continua 2022年第7期72卷 227-242页

作者： Murad A.A.Almekhlafi Taiseer Abdalla Elfadil Eisa Fahd N.Al-Wesabi Abdelzahir Abdelmaboud Manar Ahmed Hamza Abdelwahed Motwakel Ishfaq Yaseen Mohammed Rizwanullah Department of Electrical Engineering–Communication Engineering Sana’a University61101Yemen Department of Information Systems-Girls Section King Khalid UniversityMahayil62529Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Information Systems King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharj16278Saudi Arabia

Line-of-sight clarity and assurance are essential because they are considered the golden rule in wireless network planning,allowing the direct propagation path to connect the transmitter and receiver and retain the strength of the signal to be *** the increasing literature on the line of sight with different scenarios,no comprehensive study focuses on the multiplicity of parameters and basic concepts that must be taken into account when studying such a topic as it affects the results and their ***,this research aims to find limited values that ensure that the signal reaches the future efficiently and enhances the accuracy of these values’*** have designed MATLAB simulation and programming programs by Visual *** for a semi-realistic communication *** includes all the basic parameters of this system,taking into account the environment’s diversity and the characteristics of the obstacle between the transmitting station and the receiving *** we verified the correctness of the system’s ***,we begin by analyzing and studying multiple and branching cases to achieve the *** get several values from the results,which are finite values,which are a useful reference for engineers and designers of wireless networks.

关键词： Line of sight obstacle margin wireless networks communication link radio wave propagation

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Human Activity Recognition and Payload Classification for Low-Back Exoskeletons Using Deep Residual Network 4

Human Activity Recognition and Payload Classification for Lo...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

Exoskeletons created by robotics currently hold significant potential for improving the well-being and quality of life for manufacturing laborers. However, the effectiveness of existing solutions is often hindered by inadequate control systems. The limited adoption of exoskeletons in occupational settings can be attributed to their inability to adapt to users and tasks, which is a result of the constantly changing circumstances in which they are utilized. To address this issue, wearable sensors capable of being carried on the human body can employ learning-based methodologies to anticipate and classify user motion characteristics by training on extensive datasets. This study introduces a deep learning approach that utilizes inertial sensors to equip manufacturing exoskeletons with the ability to recognize human movements and adjust for payload compensation. Inertial measurement equipment can be conveniently integrated into manufacturing exoskeletons through either wearable or embeddable means. A deep residual network was employed for human action identification and categorization of lifted object weights up to 15 kg. The study achieved an average F1-score of 93.91% for activity identification and 94.35% for payload classification using the provided model. The model was trained and tested on a sample of 12 young, healthy volunteers. ©2023 IEEE.

关键词： Classification (of information)

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Free-Weight Exercise Activity Recognition using Deep Residual Neural Network based on Sensor Data from In-Ear Wearable Devices 46

Free-Weight Exercise Activity Recognition using Deep Residua...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Human activity recognition (HAR) is a popular study area in the current era of the Internet of Things and artificial intelligence. HAR approaches have been successfully applied in many real-world scenarios, such as sports tracking and assessment and remote healthcare for the elderly. Over the past decade, studies on HAR employing wearable sensors have been explored operating various wearable devices, including smartwatches, smart shoes, and other intelligent wearables. The advancement of sensor technology has directed the combining of inertial sensors into ear-worn devices, making it possible to record physical movements privately. In this study, we proposed a deep residual network named ResNeXt, for identifying free- weight exercises using sensor data from smart in-ear devices. To consider the performance of the proposed model and other deep learning models, we conducted experiments using a publicly available dataset called ERICA, which collected free-weight exercise activities from inertial sensors of both an in-ear device and a dumbbell. Our experimental results demonstrated that the suggested ResNeXt network surpassed other deep learning models, gaining the highest F1-score of 96.67% and 99.75% using inertial data from in-ear sensors and dumbbell sensors, respectively © 2023 IEEE.

关键词： Wearable sensors

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Deep Learning Approaches for Classification of Road Types Using Wearable Sensors Embedded in Smart Glasses 8

Deep Learning Approaches for Classification of Road Types Us...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Witayarat, Apiwat Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350396546

The Internet of Vehicles (IoV) is an architecture of the intelligent transportation system that combines automotive, transportation, and information exchange to increase road safety. The categorization of roadways not only improves the passenger's comfort and safety but also provides autonomous cars with safe navigation paths. In this paper, we present an identification method based on movement data from smart glasses (electroocu-lography, acceleration, and angular velocity) to categorize four kinds of roads often experienced: highway, city road, highway, undeveloped region, and housing estate. We developed a deep pyramidal residual network that automatically recovers spatial-temporal data and efficiently identifies road kinds. We performed experiments to evaluate deep learning models utilizing a publicly available benchmark dataset, including sensor data acquired from intelligent eyewear. Experimentally, we discovered that the suggested 1D-PyramidNet model obtained the most incredible interpretation with the most increased accuracy (92.23%) and outperformed all other deep learning models. © 2023 IEEE.

关键词： Wearable sensors

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Improving Real-Time Data Analysis Accuracy with Machine Learning Tools 3

Improving Real-Time Data Analysis Accuracy with Machine Lear...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Singla, Abhishek Divya, K. Manohar, K. Kantak, Ganesh Kumar Singh, Akhilendra Pratap Rajesh, A. Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350319125

Gadget studying can be used to enhance the accuracy of actual-time records evaluation. system-gaining knowledge can discover patterns and tendencies in information extra quickly and accurately while not having to manually analyze the data, allowing for decisions to be made with extra self-belief and accuracy. By means of incorporating capabilities that tune user interaction, machines gaining knowledge can use supervised and unsupervised studying to regulate the models to beautify accuracy. This paper explores the use of machine learning tools to improve the accuracy of real-time data analysis. With the constant influx of data in various industries, accurate and timely analysis is crucial for making informed decisions. However, traditional data analysis methods may fall short in handling the complexity and volume of real-time data. Machine learning algorithms offer the potential to overcome these challenges by autonomously identifying patterns and trends in real-time data. This paper discusses the benefits of incorporating machine learning in real-time data analysis and provides a comprehensive review of existing tools and techniques. Through case studies and experiments, we demonstrate the effectiveness of machine learning in improving the accuracy of real-time data analysis. The results of our study highlight the potential of machine learning tools in enhancing decision-making processes and driving better outcomes in various industries. Furthermore, we discuss the challenges and limitations of using machine learning in real-time data analysis and provide recommendations for future research in this area. This method of the usage of system gaining knowledge of tools will increase the efficiency and accuracy of real-time records evaluation and allows to make knowledgeable choices which can result in better effects. © 2023 IEEE.

关键词： Anomaly detection

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Enabling Autonomous Deep Learning Strategies for Real-Time Data Analysis in Machine Learning 3

Enabling Autonomous Deep Learning Strategies for Real-Time D...

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3rd International Conference on Smart Generation Computing, communication and Networking, SMART GENCON 2023

作者： Sahoo, Girija Shankar Sekaran, Ramesh Renuka, G. Dhabliya, Dharmesh Faujdar, Pramod Kumar Minhas, Deepak Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

This studies focuses on leveraging self-sustaining deep getting to know techniques for real-time records analysis in device getting to know. We propose a machine this is enabled by means of artificial intelligence to streamline the technique of extracting significant insights from sensor-based records. Our approach considers two middle components: interpreting data with recurrent neural networks and deploying a mastering agent to routinely whole the statistics analysis process. The getting to know agent's training is based on supervised mastering, enabling it to discover and traverse styles inside the information. The recurrent neural community (RNN) offers the functionality to seize temporal styles within the information, with its capacity to make predictions based on previous input. As a result, our system can produce fairly correct insights with minimal human intervention. This makes it suitable for time-sensitive information analysis situations in the industrial area in which decisions have to be taken in actual-time. © 2023 IEEE.

关键词： Recurrent neural networks

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Improving Lower Limb Activity Recognition Based on Inertial Sensors using CNN-LSTM Network 4

Improving Lower Limb Activity Recognition Based on Inertial ...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

ISBN: (纸本)9798350330618

The utilization of wearable sensors has gained significant popularity across various domains. This widespread adoption has driven the need for advancements in sensor technology, focusing on attributes such as lightweight design, dependability, and integration capabilities. One specific area where wearable sensor networks have faced challenges is in human activity recognition, particularly in accurately inferring movement data. The effectiveness of this process depends on several factors, such as the quality of sensor-derived features, the discriminatory power of selected features, and the chosen classification methodology. To address these challenges, this research introduces a novel hybrid deep learning architecture called CNN-LSTM. The primary goal of this architecture is to enhance the accuracy of recognizing human lower limb movement. To evaluate the effectiveness of the proposed model, a series of investigations were conducted using the publicly available HuGaDB dataset. This dataset consists of data collected from a body sensor network comprising six wearable inertial sensors, including accelerometers and gyroscopes. These sensors were positioned on both the right and left thighs, shins, and feet. The outcomes of the investigation demonstrated that the suggested model achieved an impressive precision of 97.81% and a maximum F1-score of 95.50%. These results highlight the effectiveness of the CNN-LSTM model in accurately recognizing human lower limb movement. ©2023 IEEE.

关键词： Wearable sensors

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Improving EEG-Based Epileptic Seizures Detection Using Self-Normalizing Neural Network 4

Improving EEG-Based Epileptic Seizures Detection Using Self-...

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4th International Conference on Big Data Analytics and Practices, IBDAP 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350300192

Epilepsy, a neurological disorder, is typically identified by analyzing electroencephalogram (EEG) signals. However, manually inspecting epileptic seizures is a time-consuming and labor-intensive task. Previous automatic detection algorithms utilizing traditional methods have demonstrated good accuracy in specific EEG classification scenarios but have shown poor performance in others. To tackle this challenge, our study introduces a new deep learning model called the self-normalizing neural network. This model leverages convolutional neural networks and recurrent neural networks to enhance the detection of epileptic seizures using EEG signals. To evaluate our approach, we designed and assessed the proposed detection model using a publicly available Epileptic Seizures Recognition Dataset (ESRD). The experimental findings indicate that the self-normalizing neural network achieves a performance improvement of up to 1.03% accuracy and 1.78% F1-score in epileptic seizure detection, surpassing traditional convolutional neural network approaches. © 2023 IEEE.

关键词： Electroencephalography

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Deep Learning Networks for Complex Activity Recognition Based on Wrist-Worn Sensor 38

Deep Learning Networks for Complex Activity Recognition Base...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350302196

Wearable smart devices, such as smartphones and smartwatches, offer great potential as platforms for automated human action identification. However, accurately monitoring complex human actions on these devices poses a challenge due to the presence of similarities in patterns across different actions. This occurs when distinct human actions exhibit comparable signal patterns or characteristics. The placement of motion sensors on the body plays a crucial role in detecting human behavior. Typically, wearable sensors placed at the trouser pocket or a similar location are used for this purpose. However, this positioning is not suitable for identifying actions involving manual gestures. To address this, wrist-worn motion sensors are employed to detect these specific behaviors. This study aims to investigate the effectiveness of deep learning models in accurately categorizing complex human actions using sensor data from wrist-worn devices. Nine deep learning models utilizing convolutional neural networks and recurrent neural networks were examined for their identification capabilities. The models were evaluated using the WHARF dataset, a publicly available benchmark dataset for human activity recognition. The investigation revealed that the proposed CNN-BiGRU model outperformed other deep learning models, achieving an accuracy rate of 87.20% and an Fl-score of 84.46%. © 2023 IEEE.

关键词： Wearable sensors

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