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Enhancing Sensor-based Human Activity Recognition Using Hybrid Deep Learning and Data Augmentation 5

Enhancing Sensor-based Human Activity Recognition Using Hybr...

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda 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 Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798331505998

Human Activity Recognition (HAR) is essential in various applications, including wellness tracking, automated residences, and fitness monitoring. In the past few decades, sensor-based HAR has become increasingly popular due to advancements in technologies for sensors. Nevertheless, HAR networks' effectiveness dramatically depends on the caliber and volume of the training data, which is frequently restricted and unevenly distributed. This research introduces a novel deep learning method called Multihead-CNN-BiGRU, which integrates one-dimensional convolutional neural networks with bidirectional gated recurrent units (BiGRU) to improve the accuracy of sensor-based HAR. To tackle the problem of insufficient a nd uneven training data, we utilize the synthetic minority over-sampling technique (SMOTE) to augment the data. The suggested model is assessed using the publicly accessible WISDM dataset, which comprises sensor data from diverse human actions. The ID-CNN is employed for extracting localized characteristics from the sensor data, while the BiGRU gathers temporal dependencies and contextual information. The hybrid architecture allows the model to acquire spatial and temporal patterns efficiently. In addition, the SMOTE technique is utilized to create artificial samples of the underrepresented classes, thus equalizing the distribution of classes and enhancing the model's capacity to generalize. The experimental findings show that our hybrid strategy provides exceptional outcomes when paired with SMOTE data augmentation. It obtains the most excellent accuracy of 99.51 % and the highest F1-score of 99.49% compared to the most advanced approaches. The suggested framework provides a reliable and precise solution for sensor-based HAR, which opens up opportunities for improved applications in other fields. © 2024 IEEE.

关键词： Spatio-temporal data

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A Sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers 9

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda 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 Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok 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)9798350318241

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers' daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers' duties and developing improved working regulations that consider their well-being and daily tasks. © 2024 IEEE.

关键词： Sanitation

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Squeeze-and-Excitation Hybrid Network for Biometric Identification via Photoplethysmography 5

Squeeze-and-Excitation Hybrid Network for Biometric Identifi...

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5th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda 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 Faculty of Applied Science Research Group in Statistical Learning and Inference Department of Applied Statistics Bangkok 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)9798331505998

Biometric identity verification is critical in security, healthcare, and personalized services. Photoplethysmography (PPG) signals, measuring blood volume changes in small vessels, offer a promising, non-invasive biometric method but often need more accuracy and robustness. To address this, we propose the ResNet-BiGRU-SE framework, which enhances PPG-based biometric identification through a s queeze-and-excitation (SE) attention mechanism. This architecture integrates convolutional neural networks (CNNs), bidirectional gated recurrent units (BiGRUs), and SE attention modules to extract spatial and temporal features from PPG signals. ResNet architecture in CNN layers extracts local patterns and mitigates vanishing gradients with residual connections, while BiGRU layers capture temporal dependencies and contextual information. The SE attention mechanism enhances focus on informative PPG signal parts by adjusting feature responses per channel. Evaluated on a standardized dataset of PPG signals from various patients, the ResNet-BiGRU-SE model demonstrates superior accuracy (95.26%), low loss (0.24), and a high F1-score (95.25 %), outper-forming recent methods. Comprehensive ablation studies confirm the model's robust and reliable performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

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

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 IEEE.

关键词： Learning systems

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Convolutional Neural Network with CBAM Module for Fitness Activity Recognition Using Wearable IMU Sensors 9

Convolutional Neural Network with CBAM Module for Fitness Ac...

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ISBN: (纸本)9798350318241

Wearable motion sensors are gaining traction for fitness activity tracking due to their utility in assisted living, wellness tracking, and fitness training applications. Such sensors typically incorporate accelerometers and gyroscopes to monitor and log physical activities. This work proposes a deep-learning approach for identifying fitness activities using wearable inertial measurement units (IMUs) signals. Specifically, a convolutional neural network (CNN) integrated with a convolutional block attention module (CBAM) provides the core framework. The CBAM module applies channel and spatial attention mechanisms to enhance CNN's capacity to discriminate salient features. Multichannel IMU time-series data, comprising accelerometer and gyroscope readings as subjects perform various exercises, supply input to the model. By learning via an attention-enhanced architecture, the model can extract robust and informative representations to categorize activities accurately. Experiments on a public IMU dataset with five exercise types across over twenty participants evaluated the approach. Results showed over 99.58% subject-independent activity recognition accuracy when augmenting the CNN with CBAM, constituting significant performance gains. The research findings indicate that an efficient deep-learning solution with attention modeling can recognize fitness activities reliably from on-body sensors. The proposed methodology holds substantial promise for applications in sports analytics and personalized health tracking. © 2024 IEEE.

关键词： Gyroscopes

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Harnessing Deep Learning for Activity Recognition in Seniors' Daily Routines with Wearable Sensors 47

Harnessing Deep Learning for Activity Recognition in Seniors...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

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

ISBN: (纸本)9798350365597

Wearable sensors are increasingly popular for monitoring the daily activities of older individuals, sparking significant interest in human activity recognition (HAR). Accurate and timely identification of these activities is crucial for providing personalized care and timely interventions to maintain the well-being of elderly individuals. However, existing HAR methods often need support with complex activity patterns, limited labeled data, and the need for real-time performance. To address these challenges, this study proposes a novel deep-learning approach called ResBiGRU for precisely identifying daily activities in older adults using wearable sensors. The ResBiGRU architecture combines residual connections with BiGRU networks to capture short-term and long-term dependencies in sensor data effectively. We also propose a data augmentation strategy to improve generalization performance. Evaluation of the HAR70+ dataset, consisting of activities performed by 18 elderly individuals, demonstrates superior performance of our ResBiGRU model with an accuracy of 97.39% and an F1-score of 97.74%. Furthermore, our model supports real-time inference, making it suitable for practical applications. Implementing the ResBiGRU approach could significantly enhance monitoring and care for older adults by accurately identifying their daily activities. This research advances HAR methods tailored for elderly individuals, opening new avenues for personalized healthcare and assisted living technologies. © 2024 IEEE.

关键词： Wearable sensors

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Leveraging Residual Deep Neural Networks and Multi-Device Sensors for Heterogeneous Activity Recognition 47

Leveraging Residual Deep Neural Networks and Multi-Device Se...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

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

ISBN: (纸本)9798350365597

This study introduces a novel approach to identifying human activities using wearable sensors, particularly smart-phones and smartwatches. By leveraging deep learning neural networks and data from the HHAR dataset, which includes accelerometer and gyroscope data from individuals engaged in various activities, our method, centered around the HAR-Res NeXt model, accurately detects six activities. Utilizing residual connections and multi-kernel blocks, our approach effectively captures temporal and spatial relationships in sensor data. Experimental results demonstrate superior performance to standard machine learning algorithms and other deep learning approaches for human activity recognition. HAR-ResNeXt achieves high accuracy rates, particularly in classifying smartphone sensor data, underscoring its adaptability across diverse scenarios. Comparative analysis reveals the effectiveness of smartphone sensors and emphasizes the importance of multi-modal sensor fusion for accurate activity detection. © 2024 IEEE.

关键词： Smartphones

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Modelling of the Internet computer Protocol Architecture: The Next Generation Blockchain 4th

Modelling of the Internet Computer Protocol Architecture: ...

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4th International Congress on Blockchain and Applications, BLOCKCHAIN 2022

作者： Li, AoXuan Serena, Luca Zichichi, Mirko Tang, Su-Kit D’Angelo, Gabriele Ferretti, Stefano Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering University of Bologna Bologna Italy Ontology Engineering Group Universidad Politécnica de Madrid Madrid Spain Department of Pure and Applied Sciences University of Urbino "Carlo Bo" Urbino Italy

ISBN: (纸本)9783031212284

The Internet computer Protocol is described as a third-generation blockchain system that aims to provide secure and scalable distributed systems through blockchains and smart contracts. In this position paper, this innovative architecture is introduced and then discussed in view of its modeling and simulation aspects. In fact, a properly defined digital twin of the Internet computer Protocol could help its design, development, and evaluation in terms of performance and resilience to specific security attacks. To this extent, we propose a multi-level simulation model that follows an agent-based paradigm. The main issues of the modeling and simulation, and the main expected outcomes, are described and discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain Distributed Ledger Technology Internet computer Modelling and simulation

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Development of an modified Ant Colony Optimization algorithm for solving path planning problems of a robot system 10

Development of an modified Ant Colony Optimization algorithm...

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10th International Conference on Signal Processing and Integrated Networks, SPIN 2023

作者： Sharan, Sudeep Dominguez-Jimenez, Juan Jose Nauth, Peter Frankfurt University of Applied Sciences Faculty of Computer Science and Engineering Frankfurt am Main Germany University of Cádiz Ucase Software Engineering Group School of Engineering Avda. de la Universidad de Cádiz 10 Puerto Real Cádiz11519 Spain

ISBN: (纸本)9781665490993

In the modern world of high-speed technologies, where every operation needs to be performed instantaneously and more efficiently, scientists and engineers have created a Bioinspired algorithm to solve the problems encountered in realworld activities. The Ant Colony optimisation (ACO) algorithm is one such solution that assists in solving the problems of robot path planning. In this work-in-progress article, we propose a new way of using the ACO algorithm which ensures solving the problems encountered in traditional ACO algorithms. This algorithm was tested on two environments to examine output efficiency and computational output time. The results show that the proposed ACO algorithm is completely efficient in small-scale environments and remarkably better results were observed on testing it in the bigger-scale environment. The evaluations prove that the ACO algorithm for path planning can provide rapid path planning with acceptable results and for future development can be integrated with the robot system to test it in any real-world scenarios by increasing the number of ants. © 2023 IEEE.

关键词： Motion planning

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A Systematic Literature Review of Decentralized Applications in Web3: Identifying Challenges and Opportunities for Blockchain Developers

A Systematic Literature Review of Decentralized Applications...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Hossain Faruk, Md Jobair Raya, Pratusha Siam, Md Kamrul Cheng, Jerry Q. Shahriar, Hossain Cuzzocrea, Alfredo Bringas, Pablo Garcia New York Institute of Technology Department of Computer Science United States Kennesaw State University Department of Software Engineering United States University of West Florida Center for Cybersecurity United States University of Deusto D4K Group Bilbao Spain University of Calabria IDEA Lab Rende Italy University of Paris City Department of Computer Science Paris France

ISBN: (纸本)9798350362480

The Internet has opened the floor to stakeholders by redefining the way of organizing, communicating, and collaborating that was initiated by the Web's development. The advancement of the World Wide Web is an outright phenomenon and significant and we witnessed the evolution of the Web. As decentralized technologies continue to gain traction, Web3, or the decentralized internet, has emerged as a promising approach to enable a more secure, transparent, and privacy-preserved digital landscape. In this paper, we thoroughly conduct a systematic study to explore the challenges and opportunities encountered by blockchain developers in the context of decentralized applications (dApps) in Web3. We analyze a set of peer-reviewed research articles, whitepapers, and technical reports and present an in-depth understanding of the current state of Web3 development and its implications. Our finding indicates the opportunities that Web3 can facilitate, such as expanded use cases, enhanced security and privacy, decentralized infrastructure, and the potential for enabling inclusive development resources for blockchain developers. Additionally, we highlight various challenges that blockchain developers deal with including scalability, security, privacy, interoperability, and the need for standardized tools and frameworks along with various challenges in the software development lifecycle (SDLC). While there are significant challenges to overcome, the potential benefits of Web3 are substantial and could lead to a more inclusive, secure, and transparent digital ecosystem. Furthermore, we emphasize the importance of continued research, collaboration, and innovation among stakeholders to address the identified challenges and capitalize on Web3's opportunities. © 2024 IEEE.

关键词： Decentralized finance

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